WorldWideScience

Sample records for hierarchical cluster analyses

  1. Permutation Tests of Hierarchical Cluster Analyses of Carrion Communities and Their Potential Use in Forensic Entomology.

    Science.gov (United States)

    van der Ham, Joris L

    2016-05-19

    Forensic entomologists can use carrion communities' ecological succession data to estimate the postmortem interval (PMI). Permutation tests of hierarchical cluster analyses of these data provide a conceptual method to estimate part of the PMI, the post-colonization interval (post-CI). This multivariate approach produces a baseline of statistically distinct clusters that reflect changes in the carrion community composition during the decomposition process. Carrion community samples of unknown post-CIs are compared with these baseline clusters to estimate the post-CI. In this short communication, I use data from previously published studies to demonstrate the conceptual feasibility of this multivariate approach. Analyses of these data produce series of significantly distinct clusters, which represent carrion communities during 1- to 20-day periods of the decomposition process. For 33 carrion community samples, collected over an 11-day period, this approach correctly estimated the post-CI within an average range of 3.1 days.

  2. [Study of the clinical phenotype of symptomatic chronic airways disease by hierarchical cluster analysis and two-step cluster analyses].

    Science.gov (United States)

    Ning, P; Guo, Y F; Sun, T Y; Zhang, H S; Chai, D; Li, X M

    2016-09-01

    To study the distinct clinical phenotype of chronic airway diseases by hierarchical cluster analysis and two-step cluster analysis. A population sample of adult patients in Donghuamen community, Dongcheng district and Qinghe community, Haidian district, Beijing from April 2012 to January 2015, who had wheeze within the last 12 months, underwent detailed investigation, including a clinical questionnaire, pulmonary function tests, total serum IgE levels, blood eosinophil level and a peak flow diary. Nine variables were chosen as evaluating parameters, including pre-salbutamol forced expired volume in one second(FEV1)/forced vital capacity(FVC) ratio, pre-salbutamol FEV1, percentage of post-salbutamol change in FEV1, residual capacity, diffusing capacity of the lung for carbon monoxide/alveolar volume adjusted for haemoglobin level, peak expiratory flow(PEF) variability, serum IgE level, cumulative tobacco cigarette consumption (pack-years) and respiratory symptoms (cough and expectoration). Subjects' different clinical phenotype by hierarchical cluster analysis and two-step cluster analysis was identified. (1) Four clusters were identified by hierarchical cluster analysis. Cluster 1 was chronic bronchitis in smokers with normal pulmonary function. Cluster 2 was chronic bronchitis or mild chronic obstructive pulmonary disease (COPD) patients with mild airflow limitation. Cluster 3 included COPD patients with heavy smoking, poor quality of life and severe airflow limitation. Cluster 4 recognized atopic patients with mild airflow limitation, elevated serum IgE and clinical features of asthma. Significant differences were revealed regarding pre-salbutamol FEV1/FVC%, pre-salbutamol FEV1% pred, post-salbutamol change in FEV1%, maximal mid-expiratory flow curve(MMEF)% pred, carbon monoxide diffusing capacity per liter of alveolar(DLCO)/(VA)% pred, residual volume(RV)% pred, total serum IgE level, smoking history (pack-years), St.George's respiratory questionnaire

  3. Non-Hierarchical Clustering as a method to analyse an open-ended ...

    African Journals Online (AJOL)

    Apple

    tests, provide instructors with tools to probe students' conceptual knowledge of various fields of science and ... quantitative non-hierarchical clustering analysis method known as k-means (Everitt, Landau, Leese & Stahl, ...... undergraduate engineering students in creating ... mathematics-formal reasoning and the contextual.

  4. Neutrosophic Hierarchical Clustering Algoritms

    Directory of Open Access Journals (Sweden)

    Rıdvan Şahin

    2014-03-01

    Full Text Available Interval neutrosophic set (INS is a generalization of interval valued intuitionistic fuzzy set (IVIFS, whose the membership and non-membership values of elements consist of fuzzy range, while single valued neutrosophic set (SVNS is regarded as extension of intuitionistic fuzzy set (IFS. In this paper, we extend the hierarchical clustering techniques proposed for IFSs and IVIFSs to SVNSs and INSs respectively. Based on the traditional hierarchical clustering procedure, the single valued neutrosophic aggregation operator, and the basic distance measures between SVNSs, we define a single valued neutrosophic hierarchical clustering algorithm for clustering SVNSs. Then we extend the algorithm to classify an interval neutrosophic data. Finally, we present some numerical examples in order to show the effectiveness and availability of the developed clustering algorithms.

  5. Hierarchical clustering for graph visualization

    CERN Document Server

    Clémençon, Stéphan; Rossi, Fabrice; Tran, Viet Chi

    2012-01-01

    This paper describes a graph visualization methodology based on hierarchical maximal modularity clustering, with interactive and significant coarsening and refining possibilities. An application of this method to HIV epidemic analysis in Cuba is outlined.

  6. Hierarchical Formation of Galactic Clusters

    CERN Document Server

    Elmegreen, B G

    2006-01-01

    Young stellar groupings and clusters have hierarchical patterns ranging from flocculent spiral arms and star complexes on the largest scale to OB associations, OB subgroups, small loose groups, clusters and cluster subclumps on the smallest scales. There is no obvious transition in morphology at the cluster boundary, suggesting that clusters are only the inner parts of the hierarchy where stars have had enough time to mix. The power-law cluster mass function follows from this hierarchical structure: n(M_cl) M_cl^-b for b~2. This value of b is independently required by the observation that the summed IMFs from many clusters in a galaxy equals approximately the IMF of each cluster.

  7. Intuitionistic fuzzy hierarchical clustering algorithms

    Institute of Scientific and Technical Information of China (English)

    Xu Zeshui

    2009-01-01

    Intuitionistic fuzzy set (IFS) is a set of 2-tuple arguments, each of which is characterized by a mem-bership degree and a nonmembership degree. The generalized form of IFS is interval-valued intuitionistic fuzzy set (IVIFS), whose components are intervals rather than exact numbers. IFSs and IVIFSs have been found to be very useful to describe vagueness and uncertainty. However, it seems that little attention has been focused on the clus-tering analysis of IFSs and IVIFSs. An intuitionistic fuzzy hierarchical algorithm is introduced for clustering IFSs, which is based on the traditional hierarchical clustering procedure, the intuitionistic fuzzy aggregation operator, and the basic distance measures between IFSs: the Hamming distance, normalized Hamming, weighted Hamming, the Euclidean distance, the normalized Euclidean distance, and the weighted Euclidean distance. Subsequently, the algorithm is extended for clustering IVIFSs. Finally the algorithm and its extended form are applied to the classifications of building materials and enterprises respectively.

  8. Hierarchical Clustering and Active Galaxies

    CERN Document Server

    Hatziminaoglou, E; Manrique, A

    2000-01-01

    The growth of Super Massive Black Holes and the parallel development of activity in galactic nuclei are implemented in an analytic code of hierarchical clustering. The evolution of the luminosity function of quasars and AGN will be computed with special attention paid to the connection between quasars and Seyfert galaxies. One of the major interests of the model is the parallel study of quasar formation and evolution and the History of Star Formation.

  9. Galaxy formation through hierarchical clustering

    Science.gov (United States)

    White, Simon D. M.; Frenk, Carlos S.

    1991-01-01

    Analytic methods for studying the formation of galaxies by gas condensation within massive dark halos are presented. The present scheme applies to cosmogonies where structure grows through hierarchical clustering of a mixture of gas and dissipationless dark matter. The simplest models consistent with the current understanding of N-body work on dissipationless clustering, and that of numerical and analytic work on gas evolution and cooling are adopted. Standard models for the evolution of the stellar population are also employed, and new models for the way star formation heats and enriches the surrounding gas are constructed. Detailed results are presented for a cold dark matter universe with Omega = 1 and H(0) = 50 km/s/Mpc, but the present methods are applicable to other models. The present luminosity functions contain significantly more faint galaxies than are observed.

  10. PERFORMANCE OF SELECTED AGGLOMERATIVE HIERARCHICAL CLUSTERING METHODS

    Directory of Open Access Journals (Sweden)

    Nusa Erman

    2015-01-01

    Full Text Available A broad variety of different methods of agglomerative hierarchical clustering brings along problems how to choose the most appropriate method for the given data. It is well known that some methods outperform others if the analysed data have a specific structure. In the presented study we have observed the behaviour of the centroid, the median (Gower median method, and the average method (unweighted pair-group method with arithmetic mean – UPGMA; average linkage between groups. We have compared them with mostly used methods of hierarchical clustering: the minimum (single linkage clustering, the maximum (complete linkage clustering, the Ward, and the McQuitty (groups method average, weighted pair-group method using arithmetic averages - WPGMA methods. We have applied the comparison of these methods on spherical, ellipsoid, umbrella-like, “core-and-sphere”, ring-like and intertwined three-dimensional data structures. To generate the data and execute the analysis, we have used R statistical software. Results show that all seven methods are successful in finding compact, ball-shaped or ellipsoid structures when they are enough separated. Conversely, all methods except the minimum perform poor on non-homogenous, irregular and elongated ones. Especially challenging is a circular double helix structure; it is being correctly revealed only by the minimum method. We can also confirm formerly published results of other simulation studies, which usually favour average method (besides Ward method in cases when data is assumed to be fairly compact and well separated.

  11. Assembling hierarchical cluster solids with atomic precision.

    Science.gov (United States)

    Turkiewicz, Ari; Paley, Daniel W; Besara, Tiglet; Elbaz, Giselle; Pinkard, Andrew; Siegrist, Theo; Roy, Xavier

    2014-11-12

    Hierarchical solids created from the binary assembly of cobalt chalcogenide and iron oxide molecular clusters are reported. Six different molecular clusters based on the octahedral Co6E8 (E = Se or Te) and the expanded cubane Fe8O4 units are used as superatomic building blocks to construct these crystals. The formation of the solid is driven by the transfer of charge between complementary electron-donating and electron-accepting clusters in solution that crystallize as binary ionic compounds. The hierarchical structures are investigated by single-crystal X-ray diffraction, providing atomic and superatomic resolution. We report two different superstructures: a superatomic relative of the CsCl lattice type and an unusual packing arrangement based on the double-hexagonal close-packed lattice. Within these superstructures, we demonstrate various compositions and orientations of the clusters.

  12. Hesitant fuzzy agglomerative hierarchical clustering algorithms

    Science.gov (United States)

    Zhang, Xiaolu; Xu, Zeshui

    2015-02-01

    Recently, hesitant fuzzy sets (HFSs) have been studied by many researchers as a powerful tool to describe and deal with uncertain data, but relatively, very few studies focus on the clustering analysis of HFSs. In this paper, we propose a novel hesitant fuzzy agglomerative hierarchical clustering algorithm for HFSs. The algorithm considers each of the given HFSs as a unique cluster in the first stage, and then compares each pair of the HFSs by utilising the weighted Hamming distance or the weighted Euclidean distance. The two clusters with smaller distance are jointed. The procedure is then repeated time and again until the desirable number of clusters is achieved. Moreover, we extend the algorithm to cluster the interval-valued hesitant fuzzy sets, and finally illustrate the effectiveness of our clustering algorithms by experimental results.

  13. A New Metrics for Hierarchical Clustering

    Institute of Scientific and Technical Information of China (English)

    YANGGuangwen; SHIShuming; WANGDingxing

    2003-01-01

    Hierarchical clustering is a popular method of performing unsupervised learning. Some metric must be used to determine the similarity between pairs of clusters in hierarchical clustering. Traditional similarity metrics either can deal with simple shapes (i.e. spherical shapes) only or are very sensitive to outliers (the chaining effect). The main contribution of this paper is to propose some potential-based similarity metrics (APES and AMAPES) between clusters in hierarchical clustering, inspired by the concepts of the electric potential and the gravitational potential in electromagnetics and astronomy. The main features of these metrics are: the first, they have strong antijamming capability; the second, they are capable of finding clusters of different shapes such as spherical, spiral, chain, circle, sigmoid, U shape or other complex irregular shapes; the third, existing algorithms and research fruits for classical metrics can be adopted to deal with these new potential-based metrics with no or little modification. Experiments showed that the new metrics are more superior to traditional ones. Different potential functions are compared, and the sensitivity to parameters is also analyzed in this paper.

  14. Managing Clustered Data Using Hierarchical Linear Modeling

    Science.gov (United States)

    Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.

    2012-01-01

    Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…

  15. Managing Clustered Data Using Hierarchical Linear Modeling

    Science.gov (United States)

    Warne, Russell T.; Li, Yan; McKyer, E. Lisako J.; Condie, Rachel; Diep, Cassandra S.; Murano, Peter S.

    2012-01-01

    Researchers in nutrition research often use cluster or multistage sampling to gather participants for their studies. These sampling methods often produce violations of the assumption of data independence that most traditional statistics share. Hierarchical linear modeling is a statistical method that can overcome violations of the independence…

  16. Convex Clustering: An Attractive Alternative to Hierarchical Clustering

    Science.gov (United States)

    Chen, Gary K.; Chi, Eric C.; Ranola, John Michael O.; Lange, Kenneth

    2015-01-01

    The primary goal in cluster analysis is to discover natural groupings of objects. The field of cluster analysis is crowded with diverse methods that make special assumptions about data and address different scientific aims. Despite its shortcomings in accuracy, hierarchical clustering is the dominant clustering method in bioinformatics. Biologists find the trees constructed by hierarchical clustering visually appealing and in tune with their evolutionary perspective. Hierarchical clustering operates on multiple scales simultaneously. This is essential, for instance, in transcriptome data, where one may be interested in making qualitative inferences about how lower-order relationships like gene modules lead to higher-order relationships like pathways or biological processes. The recently developed method of convex clustering preserves the visual appeal of hierarchical clustering while ameliorating its propensity to make false inferences in the presence of outliers and noise. The solution paths generated by convex clustering reveal relationships between clusters that are hidden by static methods such as k-means clustering. The current paper derives and tests a novel proximal distance algorithm for minimizing the objective function of convex clustering. The algorithm separates parameters, accommodates missing data, and supports prior information on relationships. Our program CONVEXCLUSTER incorporating the algorithm is implemented on ATI and nVidia graphics processing units (GPUs) for maximal speed. Several biological examples illustrate the strengths of convex clustering and the ability of the proximal distance algorithm to handle high-dimensional problems. CONVEXCLUSTER can be freely downloaded from the UCLA Human Genetics web site at http://www.genetics.ucla.edu/software/ PMID:25965340

  17. Robust Pseudo-Hierarchical Support Vector Clustering

    DEFF Research Database (Denmark)

    Hansen, Michael Sass; Sjöstrand, Karl; Olafsdóttir, Hildur

    2007-01-01

    Support vector clustering (SVC) has proven an efficient algorithm for clustering of noisy and high-dimensional data sets, with applications within many fields of research. An inherent problem, however, has been setting the parameters of the SVC algorithm. Using the recent emergence of a method...... for calculating the entire regularization path of the support vector domain description, we propose a fast method for robust pseudo-hierarchical support vector clustering (HSVC). The method is demonstrated to work well on generated data, as well as for detecting ischemic segments from multidimensional myocardial...

  18. Principal factor and hierarchical cluster analyses for the performance assessment of an urban wastewater treatment plant in the Southeast of Spain.

    Science.gov (United States)

    Bayo, Javier; López-Castellanos, Joaquín

    2016-07-01

    Process performance and operation of wastewater treatment plants (WWTP) are carried out to ensure their compliance with legislative requirements imposed by European Union. Because a high amount of variables are daily measured, a coherent and structured approach of such a system is required to understand its inherent behavior and performance efficiency. In this sense, both principal factor analysis (PFA) and hierarchical cluster analysis (HCA) are multivariate techniques that have been widely applied to extract and structure information for different purposes. In this paper, both statistical tools are applied in an urban WWTP situated in the Southeast of Spain, a zone with special characteristics related to the geochemical background composition of water and an important use of fertilizers. Four main factors were extracted in association with nutrients, the ionic component, the organic load to the WWTP, and the efficiency of the whole process. HCA allowed distinguish between influent and effluent parameters, although a deeper examination resulted in a dendrogram with groupings similar to those previously reported for PFA.

  19. Hierarchical Control for Multiple DC Microgrids Clusters

    DEFF Research Database (Denmark)

    Shafiee, Qobad; Dragicevic, Tomislav; Vasquez, Juan Carlos;

    2014-01-01

    This paper presents a distributed hierarchical control framework to ensure reliable operation of dc Microgrid (MG) clusters. In this hierarchy, primary control is used to regulate the common bus voltage inside each MG locally. An adaptive droop method is proposed for this level which determines....... Another distributed policy is employed then to regulate the power flow among the MGs according to their local SOCs. The proposed distributed controllers on each MG communicate with only the neighbor MGs through a communication infrastructure. Finally, the small signal model is expanded for dc MG clusters...

  20. Constructing storyboards based on hierarchical clustering analysis

    Science.gov (United States)

    Hasebe, Satoshi; Sami, Mustafa M.; Muramatsu, Shogo; Kikuchi, Hisakazu

    2005-07-01

    There are growing needs for quick preview of video contents for the purpose of improving accessibility of video archives as well as reducing network traffics. In this paper, a storyboard that contains a user-specified number of keyframes is produced from a given video sequence. It is based on hierarchical cluster analysis of feature vectors that are derived from wavelet coefficients of video frames. Consistent use of extracted feature vectors is the key to avoid a repetition of computationally-intensive parsing of the same video sequence. Experimental results suggest that a significant reduction in computational time is gained by this strategy.

  1. Technique for fast and efficient hierarchical clustering

    Science.gov (United States)

    Stork, Christopher

    2013-10-08

    A fast and efficient technique for hierarchical clustering of samples in a dataset includes compressing the dataset to reduce a number of variables within each of the samples of the dataset. A nearest neighbor matrix is generated to identify nearest neighbor pairs between the samples based on differences between the variables of the samples. The samples are arranged into a hierarchy that groups the samples based on the nearest neighbor matrix. The hierarchy is rendered to a display to graphically illustrate similarities or differences between the samples.

  2. Magnetic susceptibilities of cluster-hierarchical models

    Science.gov (United States)

    McKay, Susan R.; Berker, A. Nihat

    1984-02-01

    The exact magnetic susceptibilities of hierarchical models are calculated near and away from criticality, in both the ordered and disordered phases. The mechanism and phenomenology are discussed for models with susceptibilities that are physically sensible, e.g., nondivergent away from criticality. Such models are found based upon the Niemeijer-van Leeuwen cluster renormalization. A recursion-matrix method is presented for the renormalization-group evaluation of response functions. Diagonalization of this matrix at fixed points provides simple criteria for well-behaved densities and response functions.

  3. Hierarchical Aligned Cluster Analysis for Temporal Clustering of Human Motion.

    Science.gov (United States)

    Zhou, Feng; De la Torre, Fernando; Hodgins, Jessica K

    2013-03-01

    Temporal segmentation of human motion into plausible motion primitives is central to understanding and building computational models of human motion. Several issues contribute to the challenge of discovering motion primitives: the exponential nature of all possible movement combinations, the variability in the temporal scale of human actions, and the complexity of representing articulated motion. We pose the problem of learning motion primitives as one of temporal clustering, and derive an unsupervised hierarchical bottom-up framework called hierarchical aligned cluster analysis (HACA). HACA finds a partition of a given multidimensional time series into m disjoint segments such that each segment belongs to one of k clusters. HACA combines kernel k-means with the generalized dynamic time alignment kernel to cluster time series data. Moreover, it provides a natural framework to find a low-dimensional embedding for time series. HACA is efficiently optimized with a coordinate descent strategy and dynamic programming. Experimental results on motion capture and video data demonstrate the effectiveness of HACA for segmenting complex motions and as a visualization tool. We also compare the performance of HACA to state-of-the-art algorithms for temporal clustering on data of a honey bee dance. The HACA code is available online.

  4. Extending stability through hierarchical clusters in Echo State Networks

    Directory of Open Access Journals (Sweden)

    Sarah Jarvis

    2010-07-01

    Full Text Available Echo State Networks (ESN are reservoir networks that satisfy well-established criteria for stability when constructed as feedforward networks. Recent evidence suggests that stability criteria are altered in the presence of reservoir substructures, such as clusters. Understanding how the reservoir architecture affects stability is thus important for the appropriate design of any ESN. To quantitatively determine the influence of the most relevant network parameters, we analysed the impact of reservoir substructures on stability in hierarchically clustered ESNs (HESN, as they allow a smooth transition from highly structured to increasingly homogeneous reservoirs. Previous studies used the largest eigenvalue of the reservoir connectivity matrix (spectral radius as a predictor for stable network dynamics. Here, we evaluate the impact of clusters, hierarchy and intercluster connectivity on the predictive power of the spectral radius for stability. Both hierarchy and low relative cluster sizes extend the range of spectral radius values, leading to stable networks, while increasing intercluster connectivity decreased maximal spectral radius.

  5. A Hierarchical Clustering Methodology for the Estimation of Toxicity

    Science.gov (United States)

    A Quantitative Structure Activity Relationship (QSAR) methodology based on hierarchical clustering was developed to predict toxicological endpoints. This methodology utilizes Ward's method to divide a training set into a series of structurally similar clusters. The structural sim...

  6. A combined multidimensional scaling and hierarchical clustering view for the exploratory analysis of multidimensional data

    Science.gov (United States)

    Craig, Paul; Roa-Seïler, Néna

    2013-01-01

    This paper describes a novel information visualization technique that combines multidimensional scaling and hierarchical clustering to support the exploratory analysis of multidimensional data. The technique displays the results of multidimensional scaling using a scatter plot where the proximity of any two items' representations is approximate to their similarity according to a Euclidean distance metric. The results of hierarchical clustering are overlaid onto this view by drawing smoothed outlines around each nested cluster. The difference in similarity between successive cluster combinations is used to colour code clusters and make stronger natural clusters more prominent in the display. When a cluster or group of items is selected, multidimensional scaling and hierarchical clustering are re-applied to a filtered subset of the data, and animation is used to smooth the transition between successive filtered views. As a case study we demonstrate the technique being used to analyse survey data relating to the appropriateness of different phrases to different emotionally charged situations.

  7. Hierarchical Cluster Assembly in Globally Collapsing Clouds

    CERN Document Server

    Vazquez-Semadeni, Enrique; Colin, Pedro

    2016-01-01

    We discuss the mechanism of cluster formation in a numerical simulation of a molecular cloud (MC) undergoing global hierarchical collapse (GHC). The global nature of the collapse implies that the SFR increases over time. The hierarchical nature of the collapse consists of small-scale collapses within larger-scale ones. The large-scale collapses culminate a few Myr later than the small-scale ones and consist of filamentary flows that accrete onto massive central clumps. The small-scale collapses form clumps that are embedded in the filaments and falling onto the large-scale collapse centers. The stars formed in the early, small-scale collapses share the infall motion of their parent clumps. Thus, the filaments feed both gaseous and stellar material to the massive central clump. This leads to the presence of a few older stars in a region where new protostars are forming, and also to a self-similar structure, in which each unit is composed of smaller-scale sub-units that approach each other and may merge. Becaus...

  8. Hierarchical clustering using correlation metric and spatial continuity constraint

    Science.gov (United States)

    Stork, Christopher L.; Brewer, Luke N.

    2012-10-02

    Large data sets are analyzed by hierarchical clustering using correlation as a similarity measure. This provides results that are superior to those obtained using a Euclidean distance similarity measure. A spatial continuity constraint may be applied in hierarchical clustering analysis of images.

  9. Fast, Linear Time Hierarchical Clustering using the Baire Metric

    CERN Document Server

    Contreras, Pedro

    2011-01-01

    The Baire metric induces an ultrametric on a dataset and is of linear computational complexity, contrasted with the standard quadratic time agglomerative hierarchical clustering algorithm. In this work we evaluate empirically this new approach to hierarchical clustering. We compare hierarchical clustering based on the Baire metric with (i) agglomerative hierarchical clustering, in terms of algorithm properties; (ii) generalized ultrametrics, in terms of definition; and (iii) fast clustering through k-means partititioning, in terms of quality of results. For the latter, we carry out an in depth astronomical study. We apply the Baire distance to spectrometric and photometric redshifts from the Sloan Digital Sky Survey using, in this work, about half a million astronomical objects. We want to know how well the (more costly to determine) spectrometric redshifts can predict the (more easily obtained) photometric redshifts, i.e. we seek to regress the spectrometric on the photometric redshifts, and we use clusterwi...

  10. Hierarchical Approach in Clustering to Euclidean Traveling Salesman Problem

    Science.gov (United States)

    Fajar, Abdulah; Herman, Nanna Suryana; Abu, Nur Azman; Shahib, Sahrin

    There has been growing interest in studying combinatorial optimization problems by clustering strategy, with a special emphasis on the traveling salesman problem (TSP). TSP naturally arises as a sub problem in much transportation, manufacturing and logistics application, this problem has caught much attention of mathematicians and computer scientists. A clustering approach will decompose TSP into sub graph and form cluster, so it may reduce problem size into smaller problem. Impact of hierarchical approach will be investigated to produce a better clustering strategy that fit into Euclidean TSP. Clustering strategy to Euclidean TSP consist of two main step, there are; clustering and tour construction. The significant of this research is clustering approach solution result has error less than 10% compare to best known solution (TSPLIB) and there is improvement to a hierarchical clustering algorithm in order to fit in such Euclidean TSP solution method.

  11. The Hierarchical Clustering of Tax Burden in the EU27

    Directory of Open Access Journals (Sweden)

    Simkova Nikola

    2015-09-01

    Full Text Available The issue of taxation has become more important due to a significant share of the government revenue. There are several ways of expressing the tax burden of countries. This paper describes the traditional approach as a share of tax revenue to GDP which is applied to the total taxation and the capital taxation as a part of tax systems affecting investment decisions. The implicit tax rate on capital created by Eurostat also offers a possible explanation of the tax burden on capital, so its components are analysed in detail. This study uses one of the econometric methods called the hierarchical clustering. The data on which the clustering is based comprises countries in the EU27 for the period of 1995 – 2012. The aim of this paper is to reveal clusters of countries in the EU27 with similar tax burden or tax changes. The findings suggest that mainly newly acceding countries (2004 and 2007 are in a group of countries with a low tax burden which tried to encourage investors by favourable tax rates. On the other hand, there are mostly countries from the original EU15. Some clusters may be explained by similar historical development, geographic and demographic characteristics.

  12. Hierarchical structure of the Sicilian goats revealed by Bayesian analyses of microsatellite information.

    Science.gov (United States)

    Siwek, M; Finocchiaro, R; Curik, I; Portolano, B

    2011-02-01

    Genetic structure and relationship amongst the main goat populations in Sicily (Girgentana, Derivata di Siria, Maltese and Messinese) were analysed using information from 19 microsatellite markers genotyped on 173 individuals. A posterior Bayesian approach implemented in the program STRUCTURE revealed a hierarchical structure with two clusters at the first level (Girgentana vs. Messinese, Derivata di Siria and Maltese), explaining 4.8% of variation (amovaФ(ST) estimate). Seven clusters nested within these first two clusters (further differentiations of Girgentana, Derivata di Siria and Maltese), explaining 8.5% of variation (amovaФ(SC) estimate). The analyses and methods applied in this study indicate their power to detect subtle population structure.

  13. Hierarchical Clustering and the Concept of Space Distortion.

    Science.gov (United States)

    Hubert, Lawrence; Schultz, James

    An empirical assesssment of the space distortion properties of two prototypic hierarchical clustering procedures is given in terms of an occupancy model developed from combinatorics. Using one simple example, the single-link and complete-link clustering strategies now in common use in the behavioral sciences are empirically shown to be space…

  14. The Hierarchical Distribution of Young Stellar Clusters in Nearby Galaxies

    Science.gov (United States)

    Grasha, Kathryn; Calzetti, Daniela

    2017-01-01

    We investigate the spatial distributions of young stellar clusters in six nearby galaxies to trace the large scale hierarchical star-forming structures. The six galaxies are drawn from the Legacy ExtraGalactic UV Survey (LEGUS). We quantify the strength of the clustering among stellar clusters as a function of spatial scale and age to establish the survival timescale of the substructures. We separate the clusters into different classes, compact (bound) clusters and associations (unbound), and compare the clustering among them. We find that younger star clusters are more strongly clustered over small spatial scales and that the clustering disappears rapidly for ages as young as a few tens of Myr, consistent with clusters slowly losing the fractal dimension inherited at birth from their natal molecular clouds.

  15. Hierarchical Clustering Given Confidence Intervals of Metric Distances

    CERN Document Server

    Huang, Weiyu

    2016-01-01

    This paper considers metric spaces where distances between a pair of nodes are represented by distance intervals. The goal is to study methods for the determination of hierarchical clusters, i.e., a family of nested partitions indexed by a resolution parameter, induced from the given distance intervals of the metric spaces. Our construction of hierarchical clustering methods is based on defining admissible methods to be those methods that abide to the axioms of value - nodes in a metric space with two nodes are clustered together at the convex combination of the distance bounds between them - and transformation - when both distance bounds are reduced, the output may become more clustered but not less. Two admissible methods are constructed and are shown to provide universal upper and lower bounds in the space of admissible methods. Practical implications are explored by clustering moving points via snapshots and by clustering networks representing brain structural connectivity using the lower and upper bounds...

  16. Hierarchical Compressed Sensing for Cluster Based Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Vishal Krishna Singh

    2016-02-01

    Full Text Available Data transmission consumes significant amount of energy in large scale wireless sensor networks (WSNs. In such an environment, reducing the in-network communication and distributing the load evenly over the network can reduce the overall energy consumption and maximize the network lifetime significantly. In this work, the aforementioned problem of network lifetime and uneven energy consumption in large scale wireless sensor networks is addressed. This work proposes a hierarchical compressed sensing (HCS scheme to reduce the in-network communication during the data gathering process. Co-related sensor readings are collected via a hierarchical clustering scheme. A compressed sensing (CS based data processing scheme is devised to transmit the data from the source to the sink. The proposed HCS is able to identify the optimal position for the application of CS to achieve reduced and similar number of transmissions on all the nodes in the network. An activity map is generated to validate the reduced and uniformly distributed communication load of the WSN. Based on the number of transmissions per data gathering round, the bit-hop metric model is used to analyse the overall energy consumption. Simulation results validate the efficiency of the proposed method over the existing CS based approaches.

  17. Hierarchical modeling of cluster size in wildlife surveys

    Science.gov (United States)

    Royle, J. Andrew

    2008-01-01

    Clusters or groups of individuals are the fundamental unit of observation in many wildlife sampling problems, including aerial surveys of waterfowl, marine mammals, and ungulates. Explicit accounting of cluster size in models for estimating abundance is necessary because detection of individuals within clusters is not independent and detectability of clusters is likely to increase with cluster size. This induces a cluster size bias in which the average cluster size in the sample is larger than in the population at large. Thus, failure to account for the relationship between delectability and cluster size will tend to yield a positive bias in estimates of abundance or density. I describe a hierarchical modeling framework for accounting for cluster-size bias in animal sampling. The hierarchical model consists of models for the observation process conditional on the cluster size distribution and the cluster size distribution conditional on the total number of clusters. Optionally, a spatial model can be specified that describes variation in the total number of clusters per sample unit. Parameter estimation, model selection, and criticism may be carried out using conventional likelihood-based methods. An extension of the model is described for the situation where measurable covariates at the level of the sample unit are available. Several candidate models within the proposed class are evaluated for aerial survey data on mallard ducks (Anas platyrhynchos).

  18. Update Legal Documents Using Hierarchical Ranking Models and Word Clustering

    OpenAIRE

    Pham, Minh Quang Nhat; Nguyen, Minh Le; Shimazu, Akira

    2010-01-01

    Our research addresses the task of updating legal documents when newinformation emerges. In this paper, we employ a hierarchical ranking model tothe task of updating legal documents. Word clustering features are incorporatedto the ranking models to exploit semantic relations between words. Experimentalresults on legal data built from the United States Code show that the hierarchicalranking model with word clustering outperforms baseline methods using VectorSpace Model, and word cluster-based ...

  19. MultiDendrograms: Variable-Group Agglomerative Hierarchical Clustering

    CERN Document Server

    Gomez, Sergio; Montiel, Justo; Torres, David

    2012-01-01

    MultiDendrograms is a Java-written application that computes agglomerative hierarchical clusterings of data. Starting from a distances (or weights) matrix, MultiDendrograms is able to calculate its dendrograms using the most common agglomerative hierarchical clustering methods. The application implements a variable-group algorithm that solves the non-uniqueness problem found in the standard pair-group algorithm. This problem arises when two or more minimum distances between different clusters are equal during the agglomerative process, because then different output clusterings are possible depending on the criterion used to break ties between distances. MultiDendrograms solves this problem implementing a variable-group algorithm that groups more than two clusters at the same time when ties occur.

  20. Hierarchical Overlapping Clustering of Network Data Using Cut Metrics

    CERN Document Server

    Gama, Fernando; Ribeiro, Alejandro

    2016-01-01

    A novel method to obtain hierarchical and overlapping clusters from network data -i.e., a set of nodes endowed with pairwise dissimilarities- is presented. The introduced method is hierarchical in the sense that it outputs a nested collection of groupings of the node set depending on the resolution or degree of similarity desired, and it is overlapping since it allows nodes to belong to more than one group. Our construction is rooted on the facts that a hierarchical (non-overlapping) clustering of a network can be equivalently represented by a finite ultrametric space and that a convex combination of ultrametrics results in a cut metric. By applying a hierarchical (non-overlapping) clustering method to multiple dithered versions of a given network and then convexly combining the resulting ultrametrics, we obtain a cut metric associated to the network of interest. We then show how to extract a hierarchical overlapping clustering structure from the aforementioned cut metric. Furthermore, the so-called overlappi...

  1. Performance Analysis of Hierarchical Clustering Algorithm

    Directory of Open Access Journals (Sweden)

    K.Ranjini

    2011-07-01

    Full Text Available Clustering is the classification of objects into different groups, or more precisely, the partitioning of a data set into subsets (clusters, so that the data in each subset (ideally share some common trait - often proximity according to some defined distance measure. Data clustering is a common technique for statistical data analysis, which is used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. This paper explains the implementation of agglomerative and divisive clustering algorithms applied on various types of data. The details of the victims of Tsunami in Thailand during the year 2004, was taken as the test data. Visual programming is used for implementation and running time of the algorithms using different linkages (agglomerative to different types of data are taken for analysis.

  2. Properties of hierarchically forming star clusters

    CERN Document Server

    Maschberger, Th; Bonnell, I A; Kroupa, P

    2010-01-01

    We undertake a systematic analysis of the early (< 0.5 Myr) evolution of clustering and the stellar initial mass function in turbulent fragmentation simulations. These large scale simulations for the first time offer the opportunity for a statistical analysis of IMF variations and correlations between stellar properties and cluster richness. The typical evolutionary scenario involves star formation in small-n clusters which then progressively merge; the first stars to form are seeds of massive stars and achieve a headstart in mass acquisition. These massive seeds end up in the cores of clusters and a large fraction of new stars of lower mass is formed in the outer parts of the clusters. The resulting clusters are therefore mass segregated at an age of 0.5 Myr, although the signature of mass segregation is weakened during mergers. We find that the resulting IMF has a smaller exponent (alpha=1.8-2.2) than the Salpeter value (alpha=2.35). The IMFs in subclusters are truncated at masses only somewhat larger th...

  3. Hierarchical clusters of phytoplankton variables in dammed water bodies

    Science.gov (United States)

    Silva, Eliana Costa e.; Lopes, Isabel Cristina; Correia, Aldina; Gonçalves, A. Manuela

    2017-06-01

    In this paper a dataset containing biological variables of the water column of several Portuguese reservoirs is analyzed. Hierarchical cluster analysis is used to obtain clusters of phytoplankton variables of the phylum Cyanophyta, with the objective of validating the classification of Portuguese reservoirs previewly presented in [1] which were divided into three clusters: (1) Interior Tagus and Aguieira; (2) Douro; and (3) Other rivers. Now three new clusters of Cyanophyta variables were found. Kruskal-Wallis and Mann-Whitney tests are used to compare the now obtained Cyanophyta clusters and the previous Reservoirs clusters, in order to validate the classification of the water quality of reservoirs. The amount of Cyanophyta algae present in the reservoirs from the three clusters is significantly different, which validates the previous classification.

  4. A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis

    Directory of Open Access Journals (Sweden)

    Shaoning Li

    2017-01-01

    Full Text Available In the fields of geographic information systems (GIS and remote sensing (RS, the clustering algorithm has been widely used for image segmentation, pattern recognition, and cartographic generalization. Although clustering analysis plays a key role in geospatial modelling, traditional clustering methods are limited due to computational complexity, noise resistant ability and robustness. Furthermore, traditional methods are more focused on the adjacent spatial context, which makes it hard for the clustering methods to be applied to multi-density discrete objects. In this paper, a new method, cell-dividing hierarchical clustering (CDHC, is proposed based on convex hull retraction. The main steps are as follows. First, a convex hull structure is constructed to describe the global spatial context of geospatial objects. Then, the retracting structure of each borderline is established in sequence by setting the initial parameter. The objects are split into two clusters (i.e., “sub-clusters” if the retracting structure intersects with the borderlines. Finally, clusters are repeatedly split and the initial parameter is updated until the terminate condition is satisfied. The experimental results show that CDHC separates the multi-density objects from noise sufficiently and also reduces complexity compared to the traditional agglomerative hierarchical clustering algorithm.

  5. Hierarchical clustering techniques for image database organization and summarization

    Science.gov (United States)

    Vellaikal, Asha; Kuo, C.-C. Jay

    1998-10-01

    This paper investigates clustering techniques as a method of organizing image databases to support popular visual management functions such as searching, browsing and navigation. Different types of hierarchical agglomerative clustering techniques are studied as a method of organizing features space as well as summarizing image groups by the selection of a few appropriate representatives. Retrieval performance using both single and multiple level hierarchies are experimented with and the algorithms show an interesting relationship between the top k correct retrievals and the number of comparisons required. Some arguments are given to support the use of such cluster-based techniques for managing distributed image databases.

  6. Breaking the hierarchy - a new cluster selection mechanism for hierarchical clustering methods

    Directory of Open Access Journals (Sweden)

    Zweig Katharina A

    2009-10-01

    Full Text Available Abstract Background Hierarchical clustering methods like Ward's method have been used since decades to understand biological and chemical data sets. In order to get a partition of the data set, it is necessary to choose an optimal level of the hierarchy by a so-called level selection algorithm. In 2005, a new kind of hierarchical clustering method was introduced by Palla et al. that differs in two ways from Ward's method: it can be used on data on which no full similarity matrix is defined and it can produce overlapping clusters, i.e., allow for multiple membership of items in clusters. These features are optimal for biological and chemical data sets but until now no level selection algorithm has been published for this method. Results In this article we provide a general selection scheme, the level independent clustering selection method, called LInCS. With it, clusters can be selected from any level in quadratic time with respect to the number of clusters. Since hierarchically clustered data is not necessarily associated with a similarity measure, the selection is based on a graph theoretic notion of cohesive clusters. We present results of our method on two data sets, a set of drug like molecules and set of protein-protein interaction (PPI data. In both cases the method provides a clustering with very good sensitivity and specificity values according to a given reference clustering. Moreover, we can show for the PPI data set that our graph theoretic cohesiveness measure indeed chooses biologically homogeneous clusters and disregards inhomogeneous ones in most cases. We finally discuss how the method can be generalized to other hierarchical clustering methods to allow for a level independent cluster selection. Conclusion Using our new cluster selection method together with the method by Palla et al. provides a new interesting clustering mechanism that allows to compute overlapping clusters, which is especially valuable for biological and

  7. A Framework for Hierarchical Clustering Based Indexing in Search Engines

    Directory of Open Access Journals (Sweden)

    Parul Gupta

    2011-01-01

    Full Text Available Granting efficient and fast accesses to the index is a key issuefor performances of Web Search Engines. In order to enhancememory utilization and favor fast query resolution, WSEs useInverted File (IF indexes that consist of an array of theposting lists where each posting list is associated with a termand contains the term as well as the identifiers of the documentscontaining the term. Since the document identifiers are stored insorted order, they can be stored as the difference between thesuccessive documents so as to reduce the size of the index. Thispaper describes a clustering algorithm that aims atpartitioning the set of documents into ordered clusters so thatthe documents within the same cluster are similar and are beingassigned the closer document identifiers. Thus the averagevalue of the differences between the successive documents willbe minimized and hence storage space would be saved. Thepaper further presents the extension of this clustering algorithmto be applied for the hierarchical clustering in which similarclusters are clubbed to form a mega cluster and similar megaclusters are then combined to form super cluster. Thus thepaper describes the different levels of clustering whichoptimizes the search process by directing the searchto a specific path from higher levels of clustering to the lowerlevels i.e. from super clusters to mega clusters, then to clustersand finally to the individual documents so that the user gets thebest possible matching results in minimum possible time.

  8. D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

    Science.gov (United States)

    Suhaibah, A.; Uznir, U.; Anton, F.; Mioc, D.; Rahman, A. A.

    2016-06-01

    Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D) method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

  9. Active Clustering: Robust and Efficient Hierarchical Clustering using Adaptively Selected Similarities

    CERN Document Server

    Eriksson, Brian; Singh, Aarti; Nowak, Robert

    2011-01-01

    Hierarchical clustering based on pairwise similarities is a common tool used in a broad range of scientific applications. However, in many problems it may be expensive to obtain or compute similarities between the items to be clustered. This paper investigates the hierarchical clustering of N items based on a small subset of pairwise similarities, significantly less than the complete set of N(N-1)/2 similarities. First, we show that if the intracluster similarities exceed intercluster similarities, then it is possible to correctly determine the hierarchical clustering from as few as 3N log N similarities. We demonstrate this order of magnitude savings in the number of pairwise similarities necessitates sequentially selecting which similarities to obtain in an adaptive fashion, rather than picking them at random. We then propose an active clustering method that is robust to a limited fraction of anomalous similarities, and show how even in the presence of these noisy similarity values we can resolve the hierar...

  10. Hierarchical Cluster Analysis – Various Approaches to Data Preparation

    Directory of Open Access Journals (Sweden)

    Z. Pacáková

    2013-09-01

    Full Text Available The article deals with two various approaches to data preparation to avoid multicollinearity. The aim of the article is to find similarities among the e-communication level of EU states using hierarchical cluster analysis. The original set of fourteen indicators was first reduced on the basis of correlation analysis while in case of high correlation indicator of higher variability was included in further analysis. Secondly the data were transformed using principal component analysis while the principal components are poorly correlated. For further analysis five principal components explaining about 92% of variance were selected. Hierarchical cluster analysis was performed both based on the reduced data set and the principal component scores. Both times three clusters were assumed following Pseudo t-Squared and Pseudo F Statistic, but the final clusters were not identical. An important characteristic to compare the two results found was to look at the proportion of variance accounted for by the clusters which was about ten percent higher for the principal component scores (57.8% compared to 47%. Therefore it can be stated, that in case of using principal component scores as an input variables for cluster analysis with explained proportion high enough (about 92% for in our analysis, the loss of information is lower compared to data reduction on the basis of correlation analysis.

  11. Concept Association and Hierarchical Hamming Clustering Model in Text Classification

    Institute of Scientific and Technical Information of China (English)

    Su Gui-yang; Li Jian-hua; Ma Ying-hua; Li Sheng-hong; Yin Zhong-hang

    2004-01-01

    We propose two models in this paper. The concept of association model is put forward to obtain the co-occurrence relationships among keywords in the documents and the hierarchical Hamming clustering model is used to reduce the dimensionality of the category feature vector space which can solve the problem of the extremely high dimensionality of the documents' feature space. The results of experiment indicate that it can obtain the co-occurrence relations among keywords in the documents which promote the recall of classification system effectively. The hierarchical Hamming clustering model can reduce the dimensionality of the category feature vector efficiently, the size of the vector space is only about 10% of the primary dimensionality.

  12. Global Considerations in Hierarchical Clustering Reveal Meaningful Patterns in Data

    Science.gov (United States)

    Varshavsky, Roy; Horn, David; Linial, Michal

    2008-01-01

    Background A hierarchy, characterized by tree-like relationships, is a natural method of organizing data in various domains. When considering an unsupervised machine learning routine, such as clustering, a bottom-up hierarchical (BU, agglomerative) algorithm is used as a default and is often the only method applied. Methodology/Principal Findings We show that hierarchical clustering that involve global considerations, such as top-down (TD, divisive), or glocal (global-local) algorithms are better suited to reveal meaningful patterns in the data. This is demonstrated, by testing the correspondence between the results of several algorithms (TD, glocal and BU) and the correct annotations provided by experts. The correspondence was tested in multiple domains including gene expression experiments, stock trade records and functional protein families. The performance of each of the algorithms is evaluated by statistical criteria that are assigned to clusters (nodes of the hierarchy tree) based on expert-labeled data. Whereas TD algorithms perform better on global patterns, BU algorithms perform well and are advantageous when finer granularity of the data is sought. In addition, a novel TD algorithm that is based on genuine density of the data points is presented and is shown to outperform other divisive and agglomerative methods. Application of the algorithm to more than 500 protein sequences belonging to ion-channels illustrates the potential of the method for inferring overlooked functional annotations. ClustTree, a graphical Matlab toolbox for applying various hierarchical clustering algorithms and testing their quality is made available. Conclusions Although currently rarely used, global approaches, in particular, TD or glocal algorithms, should be considered in the exploratory process of clustering. In general, applying unsupervised clustering methods can leverage the quality of manually-created mapping of proteins families. As demonstrated, it can also provide

  13. Non-hierarchical clustering methods on factorial subspaces

    OpenAIRE

    Tortora, Cristina

    2011-01-01

    Cluster analysis (CA) aims at finding homogeneous group of individuals, where homogeneous is referred to individuals that present similar characteristics. Many CA techniques already exist, among the non-hierarchical ones the most known, thank to its simplicity and computational property, is k-means method. However, the method is unstable when the number of variables is large and when variables are correlated. This problem leads to the development of two-step methods, they perform a linear tra...

  14. Image Segmentation by Hierarchical Spatial and Color Spaces Clustering

    Institute of Scientific and Technical Information of China (English)

    YU Wei

    2005-01-01

    Image segmentation, as a basic building block for many high-level image analysis problems, has attracted many research attentions over years. Existing approaches, however, are mainly focusing on the clustering analysis in the single channel information, i.e., either in color or spatial space, which may lead to unsatisfactory segmentation performance. Considering the spatial and color spaces jointly, this paper proposes a new hierarchical image segmentation algorithm, which alternately clusters the image regions in color and spatial spaces in a fine to coarse manner. Without losing the perceptual consistence, the proposed algorithm achieves the segmentation result using only very few number of colors according to user specification.

  15. A fast quad-tree based two dimensional hierarchical clustering.

    Science.gov (United States)

    Rajadurai, Priscilla; Sankaranarayanan, Swamynathan

    2012-01-01

    Recently, microarray technologies have become a robust technique in the area of genomics. An important step in the analysis of gene expression data is the identification of groups of genes disclosing analogous expression patterns. Cluster analysis partitions a given dataset into groups based on specified features. Euclidean distance is a widely used similarity measure for gene expression data that considers the amount of changes in gene expression. However, the huge number of genes and the intricacy of biological networks have highly increased the challenges of comprehending and interpreting the resulting group of data, increasing processing time. The proposed technique focuses on a QT based fast 2-dimensional hierarchical clustering algorithm to perform clustering. The construction of the closest pair data structure is an each level is an important time factor, which determines the processing time of clustering. The proposed model reduces the processing time and improves analysis of gene expression data.

  16. Multi-mode clustering model for hierarchical wireless sensor networks

    Science.gov (United States)

    Hu, Xiangdong; Li, Yongfu; Xu, Huifen

    2017-03-01

    The topology management, i.e., clusters maintenance, of wireless sensor networks (WSNs) is still a challenge due to its numerous nodes, diverse application scenarios and limited resources as well as complex dynamics. To address this issue, a multi-mode clustering model (M2 CM) is proposed to maintain the clusters for hierarchical WSNs in this study. In particular, unlike the traditional time-trigger model based on the whole-network and periodic style, the M2 CM is proposed based on the local and event-trigger operations. In addition, an adaptive local maintenance algorithm is designed for the broken clusters in the WSNs using the spatial-temporal demand changes accordingly. Numerical experiments are performed using the NS2 network simulation platform. Results validate the effectiveness of the proposed model with respect to the network maintenance costs, node energy consumption and transmitted data as well as the network lifetime.

  17. Globular cluster formation with multiple stellar populations from hierarchical star cluster complexes

    Science.gov (United States)

    Bekki, Kenji

    2017-01-01

    Most old globular clusters (GCs) in the Galaxy are observed to have internal chemical abundance spreads in light elements. We discuss a new GC formation scenario based on hierarchical star formation within fractal molecular clouds. In the new scenario, a cluster of bound and unbound star clusters (`star cluster complex', SCC) that have a power-law cluster mass function with a slope (β) of 2 is first formed from a massive gas clump developed in a dwarf galaxy. Such cluster complexes and β = 2 are observed and expected from hierarchical star formation. The most massive star cluster (`main cluster'), which is the progenitor of a GC, can accrete gas ejected from asymptotic giant branch (AGB) stars initially in the cluster and other low-mass clusters before the clusters are tidally stripped or destroyed to become field stars in the dwarf. The SCC is initially embedded in a giant gas hole created by numerous supernovae of the SCC so that cold gas outside the hole can be accreted onto the main cluster later. New stars formed from the accreted gas have chemical abundances that are different from those of the original SCC. Using hydrodynamical simulations of GC formation based on this scenario, we show that the main cluster with the initial mass as large as [2 - 5] × 105M⊙ can accrete more than 105M⊙ gas from AGB stars of the SCC. We suggest that merging of hierarchical star cluster complexes can play key roles in stellar halo formation around GCs and self-enrichment processes in the early phase of GC formation.

  18. Hierarchically Clustered Star Formation in the Magellanic Clouds

    CERN Document Server

    Gouliermis, Dimitrios A; Ossenkopf, Volker; Klessen, Ralf S; Dolphin, Andrew E

    2012-01-01

    We present a cluster analysis of the bright main-sequence and faint pre--main-sequence stellar populations of a field ~ 90 x 90 pc centered on the HII region NGC 346/N66 in the Small Magellanic Cloud, from imaging with HST/ACS. We extend our earlier analysis on the stellar cluster population in the region to characterize the structuring behavior of young stars in the region as a whole with the use of stellar density maps interpreted through techniques designed for the study of the ISM structuring. In particular, we demonstrate with Cartwrigth & Whitworth's Q parameter, dendrograms, and the Delta-variance wavelet transform technique that the young stellar populations in the region NGC 346/N66 are hierarchically clustered, in agreement with other regions in the Magellanic Clouds observed with HST. The origin of this hierarchy is currently under investigation.

  19. Multiscale stochastic hierarchical image segmentation by spectral clustering

    Institute of Scientific and Technical Information of China (English)

    LI XiaoBin; TIAN Zheng

    2007-01-01

    This paper proposes a sampling based hierarchical approach for solving the computational demands of the spectral clustering methods when applied to the problem of image segmentation. The authors first define the distance between a pixel and a cluster, and then derive a new theorem to estimate the number of samples needed for clustering. Finally, by introducing a scale parameter into the similarity function, a novel spectral clustering based image segmentation method has been developed. An important characteristic of the approach is that in the course of image segmentation one needs not only to tune the scale parameter to merge the small size clusters or split the large size clusters but also take samples from the data set at the different scales. The multiscale and stochastic nature makes it feasible to apply the method to very large grouping problem. In addition, it also makes the segmentation compute in time that is linear in the size of the image. The experimental results on various synthetic and real world images show the effectiveness of the approach.

  20. An agglomerative hierarchical approach to visualization in Bayesian clustering problems.

    Science.gov (United States)

    Dawson, K J; Belkhir, K

    2009-07-01

    Clustering problems (including the clustering of individuals into outcrossing populations, hybrid generations, full-sib families and selfing lines) have recently received much attention in population genetics. In these clustering problems, the parameter of interest is a partition of the set of sampled individuals--the sample partition. In a fully Bayesian approach to clustering problems of this type, our knowledge about the sample partition is represented by a probability distribution on the space of possible sample partitions. As the number of possible partitions grows very rapidly with the sample size, we cannot visualize this probability distribution in its entirety, unless the sample is very small. As a solution to this visualization problem, we recommend using an agglomerative hierarchical clustering algorithm, which we call the exact linkage algorithm. This algorithm is a special case of the maximin clustering algorithm that we introduced previously. The exact linkage algorithm is now implemented in our software package PartitionView. The exact linkage algorithm takes the posterior co-assignment probabilities as input and yields as output a rooted binary tree, or more generally, a forest of such trees. Each node of this forest defines a set of individuals, and the node height is the posterior co-assignment probability of this set. This provides a useful visual representation of the uncertainty associated with the assignment of individuals to categories. It is also a useful starting point for a more detailed exploration of the posterior distribution in terms of the co-assignment probabilities.

  1. Clustering-based classification of road traffic accidents using hierarchical clustering and artificial neural networks.

    Science.gov (United States)

    Taamneh, Madhar; Taamneh, Salah; Alkheder, Sharaf

    2017-09-01

    Artificial neural networks (ANNs) have been widely used in predicting the severity of road traffic crashes. All available information about previously occurred accidents is typically used for building a single prediction model (i.e., classifier). Too little attention has been paid to the differences between these accidents, leading, in most cases, to build less accurate predictors. Hierarchical clustering is a well-known clustering method that seeks to group data by creating a hierarchy of clusters. Using hierarchical clustering and ANNs, a clustering-based classification approach for predicting the injury severity of road traffic accidents was proposed. About 6000 road accidents occurred over a six-year period from 2008 to 2013 in Abu Dhabi were used throughout this study. In order to reduce the amount of variation in data, hierarchical clustering was applied on the data set to organize it into six different forms, each with different number of clusters (i.e., clusters from 1 to 6). Two ANN models were subsequently built for each cluster of accidents in each generated form. The first model was built and validated using all accidents (training set), whereas only 66% of the accidents were used to build the second model, and the remaining 34% were used to test it (percentage split). Finally, the weighted average accuracy was computed for each type of models in each from of data. The results show that when testing the models using the training set, clustering prior to classification achieves (11%-16%) more accuracy than without using clustering, while the percentage split achieves (2%-5%) more accuracy. The results also suggest that partitioning the accidents into six clusters achieves the best accuracy if both types of models are taken into account.

  2. Kinematic gait patterns in healthy runners: A hierarchical cluster analysis.

    Science.gov (United States)

    Phinyomark, Angkoon; Osis, Sean; Hettinga, Blayne A; Ferber, Reed

    2015-11-01

    Previous studies have demonstrated distinct clusters of gait patterns in both healthy and pathological groups, suggesting that different movement strategies may be represented. However, these studies have used discrete time point variables and usually focused on only one specific joint and plane of motion. Therefore, the first purpose of this study was to determine if running gait patterns for healthy subjects could be classified into homogeneous subgroups using three-dimensional kinematic data from the ankle, knee, and hip joints. The second purpose was to identify differences in joint kinematics between these groups. The third purpose was to investigate the practical implications of clustering healthy subjects by comparing these kinematics with runners experiencing patellofemoral pain (PFP). A principal component analysis (PCA) was used to reduce the dimensionality of the entire gait waveform data and then a hierarchical cluster analysis (HCA) determined group sets of similar gait patterns and homogeneous clusters. The results show two distinct running gait patterns were found with the main between-group differences occurring in frontal and sagittal plane knee angles (Pgait strategies. These results suggest care must be taken when selecting samples of subjects in order to investigate the pathomechanics of injured runners.

  3. Hierarchical star cluster assembly in globally collapsing molecular clouds

    Science.gov (United States)

    Vázquez-Semadeni, Enrique; González-Samaniego, Alejandro; Colín, Pedro

    2017-05-01

    We discuss the mechanism of cluster formation in a numerical simulation of a molecular cloud (MC) undergoing global hierarchical collapse, focusing on how the gas motions in the parent cloud control the assembly of the cluster. The global collapse implies that the star formation rate (SFR) increases over time. The collapse is hierarchical because it consists of small-scale collapses within larger scale ones. The latter culminate a few Myr later than the first small-scale ones and consist of filamentary flows that accrete on to massive central clumps. The small-scale collapses consist of clumps that are embedded in the filaments and falling on to the large-scale collapse centres. The stars formed in the early, small-scale collapses share the infall motion of their parent clumps, so that the filaments feed both gas and stars to the massive central clump. This process leads to the presence of a few older stars in a region where new protostars are forming, and also to a self-similar structure, in which each unit is composed of smaller scale subunits that approach each other and may merge. Because the older stars formed in the filaments share the infall motion of the gas on to the central clump, they tend to have larger velocities and to be distributed over larger areas than the younger stars formed in the central clump. Finally, interpreting the initial mass function (IMF) simply as a probability distribution implies that massive stars only form once the local SFR is large enough to sample the IMF up to high masses. In combination with the increase of the SFR, this implies that massive stars tend to appear late in the evolution of the MC, and only in the central massive clumps. We discuss the correspondence of these features with observed properties of young stellar clusters, finding very good qualitative agreement.

  4. Exploiting Homogeneity of Density in Incremental Hierarchical Clustering

    Directory of Open Access Journals (Sweden)

    Dwi H. Widiyantoro

    2006-11-01

    Full Text Available Hierarchical clustering is an important tool in many applications. As it involves a large data set that proliferates over time, reclustering the data set periodically is not an efficient process. Therefore, the ability to incorporate a new data set incrementally into an existing hierarchy becomes increasingly demanding. This article describes Homogen, a system that employs a new algorithm for generating a hierarchy of concepts and clusters incrementally from a stream of observations. The system aims to construct a hierarchy that satisfies the homogeneity and the monotonicity properties. Working in a bottom-up fashion, a new observation is placed in the hierarchy and a sequence of hierarchy restructuring processes is performed only in regions that have been affected by the presence of the new observation. Additionally, it combines multiple restructuring techniques that address different restructuring objectives to get a synergistic effect. The system has been tested on a variety of domains including structured and unstructured data sets. The experimental results reveal that the system is able to construct a concept hierarchy that is consistent regardless of the input data order and whose quality is comparable to the quality of those produced by non incremental clustering algorithms.

  5. Prioritizing the risk of plant pests by clustering methods; self-organising maps, k-means and hierarchical clustering

    Directory of Open Access Journals (Sweden)

    Susan Worner

    2013-09-01

    Full Text Available For greater preparedness, pest risk assessors are required to prioritise long lists of pest species with potential to establish and cause significant impact in an endangered area. Such prioritization is often qualitative, subjective, and sometimes biased, relying mostly on expert and stakeholder consultation. In recent years, cluster based analyses have been used to investigate regional pest species assemblages or pest profiles to indicate the risk of new organism establishment. Such an approach is based on the premise that the co-occurrence of well-known global invasive pest species in a region is not random, and that the pest species profile or assemblage integrates complex functional relationships that are difficult to tease apart. In other words, the assemblage can help identify and prioritise species that pose a threat in a target region. A computational intelligence method called a Kohonen self-organizing map (SOM, a type of artificial neural network, was the first clustering method applied to analyse assemblages of invasive pests. The SOM is a well known dimension reduction and visualization method especially useful for high dimensional data that more conventional clustering methods may not analyse suitably. Like all clustering algorithms, the SOM can give details of clusters that identify regions with similar pest assemblages, possible donor and recipient regions. More important, however SOM connection weights that result from the analysis can be used to rank the strength of association of each species within each regional assemblage. Species with high weights that are not already established in the target region are identified as high risk. However, the SOM analysis is only the first step in a process to assess risk to be used alongside or incorporated within other measures. Here we illustrate the application of SOM analyses in a range of contexts in invasive species risk assessment, and discuss other clustering methods such as k

  6. Lyman Alpha Emitters in the Hierarchically Clustering Galaxy Formation

    CERN Document Server

    Kobayashi, Masakazu A R; Nagashima, Masahiro

    2007-01-01

    We present a new theoretical model for the luminosity functions (LFs) of Lyman alpha (Lya) emitting galaxies in the framework of hierarchical galaxy formation. We extend a semi-analytic model of galaxy formation that reproduces a number of observations for local galaxies, without changing the original model parameters but introducing a physically-motivated modelling to describe the escape fraction of Lya photons from host galaxies (f_esc). Though a previous study using a hierarchical clustering model simply assumed a constant and universal value of f_esc, we incorporate two new effects on f_esc: extinction by interstellar dust and galaxy-scale outflow induced as a star formation feedback. It is found that the new model nicely reproduces all the observed Lya LFs of the Lya emitters (LAEs) at different redshifts in z ~ 3--6. Our model predicts that galaxies with strong outflows and f_esc ~ 1 are dominant in the observed LFs, which is consistent with available observations while the simple universal f_esc model ...

  7. The structure of dark matter halos in hierarchical clustering theories

    CERN Document Server

    Subramanian, K; Ostriker, J P; Subramanian, Kandaswamy; Cen, Renyue; Ostriker, Jeremiah P.

    1999-01-01

    During hierarchical clustering, smaller masses generally collapse earlier than larger masses and so are denser on the average. The core of a small mass halo could be dense enough to resist disruption and survive undigested, when it is incorporated into a bigger object. We explore the possibility that a nested sequence of undigested cores in the center of the halo, which have survived the hierarchical, inhomogeneous collapse to form larger and larger objects, determines the halo structure in the inner regions. For a flat universe with $P(k) \\propto k^n$, scaling arguments then suggest that the core density profile is, $\\rho \\propto r^{-\\alpha}$ with $\\alpha = (9+3n)/(5+n)$. But whether such behaviour obtains depends on detailed dynamics. We first examine the dynamics using a fluid approach to the self-similar collapse solutions for the dark matter phase space density, including the effect of velocity dispersions. We highlight the importance of tangential velocity dispersions to obtain density profiles shallowe...

  8. Hand Tracking based on Hierarchical Clustering of Range Data

    CERN Document Server

    Cespi, Roberto; Lindner, Marvin

    2011-01-01

    Fast and robust hand segmentation and tracking is an essential basis for gesture recognition and thus an important component for contact-less human-computer interaction (HCI). Hand gesture recognition based on 2D video data has been intensively investigated. However, in practical scenarios purely intensity based approaches suffer from uncontrollable environmental conditions like cluttered background colors. In this paper we present a real-time hand segmentation and tracking algorithm using Time-of-Flight (ToF) range cameras and intensity data. The intensity and range information is fused into one pixel value, representing its combined intensity-depth homogeneity. The scene is hierarchically clustered using a GPU based parallel merging algorithm, allowing a robust identification of both hands even for inhomogeneous backgrounds. After the detection, both hands are tracked on the CPU. Our tracking algorithm can cope with the situation that one hand is temporarily covered by the other hand.

  9. Identifying Reference Objects by Hierarchical Clustering in Java Environment

    Directory of Open Access Journals (Sweden)

    RAHUL SAHA

    2011-09-01

    Full Text Available Recently Java programming environment has become so popular. Java programming language is a language that is designed to be portable enough to be executed in wide range of computers ranging from cell phones to supercomputers. Computer programs written in Java are compiled into Java Byte code instructions that are suitable for execution by a Java Virtual Machine implementation. Java virtual Machine is commonly implemented in software by means of an interpreter for the Java Virtual Machine instruction set. As an object oriented language, Java utilizes the concept of objects. Our idea is to identify the candidate objects references in a Java environment through hierarchical cluster analysis using reference stack and execution stack.

  10. The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS

    NARCIS (Netherlands)

    Zhou, Q.; Leng, F.; Leydesdorff, L.

    2015-01-01

    Purpose: To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach: We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare the

  11. The reflection of hierarchical cluster analysis of co-occurrence matrices in SPSS

    NARCIS (Netherlands)

    Zhou, Q.; Leng, F.; Leydesdorff, L.

    2015-01-01

    Purpose: To discuss the problems arising from hierarchical cluster analysis of co-occurrence matrices in SPSS, and the corresponding solutions. Design/methodology/approach: We design different methods of using the SPSS hierarchical clustering module for co-occurrence matrices in order to compare

  12. A Framework for Analyzing Software Quality using Hierarchical Clustering

    Directory of Open Access Journals (Sweden)

    Arashdeep Kaur

    2011-02-01

    Full Text Available Fault proneness data available in the early software life cycle from previous releases or similar kind of projects will aid in improving software quality estimations. Various techniques have been proposed in the literature which includes statistical method, machine learning methods, neural network techniques and clustering techniques for the prediction of faulty and non faulty modules in the project. In this study, Hierarchical clustering algorithm is being trained and tested with lifecycle data collected from NASA projects namely, CM1, PC1 and JM1 as predictive models. These predictive models contain requirement metrics and static code metrics. We have combined requirement metric model with static code metric model to get fusion metric model. Further we have investigated that which of the three prediction models is found to be the best prediction model on the basis of fault detection. The basic hypothesis of software quality estimation is that automatic quality prediction models enable verificationexperts to concentrate their attention and resources at problem areas of the system under development. The proposed approach has been implemented in MATLAB 7.4. The results show that when all the prediction techniques are evaluated, the best prediction model is found to be the fusion metric model. This proposed model is also compared with other quality models available in the literature and is found to be efficient for predicting faulty modules.

  13. Determination of genetic structure of germplasm collections: are traditional hierarchical clustering methods appropriate for molecular marker data?

    NARCIS (Netherlands)

    Odong, T.L.; Heerwaarden, van J.; Jansen, J.; Hintum, van T.J.L.; Eeuwijk, van F.A.

    2011-01-01

    Despite the availability of newer approaches, traditional hierarchical clustering remains very popular in genetic diversity studies in plants. However, little is known about its suitability for molecular marker data. We studied the performance of traditional hierarchical clustering techniques using

  14. Hierarchical clusters in families with type 2 diabetes

    Science.gov (United States)

    García-Solano, Beatriz; Gallegos-Cabriales, Esther C; Gómez-Meza, Marco V; García-Madrid, Guillermina; Flores-Merlo, Marcela; García-Solano, Mauro

    2015-01-01

    Families represent more than a set of individuals; family is more than a sum of its individual members. With this classification, nurses can identify the family health-illness beliefs obey family as a unit concept, and plan family inclusion into the type 2 diabetes treatment, whom is not considered in public policy, despite families share diet, exercise, and self-monitoring with a member who suffers type 2 diabetes. The aim of this study was to determine whether the characteristics, functionality, routines, and family and individual health in type 2 diabetes describes the differences and similarities between families to consider them as a unit. We performed an exploratory, descriptive hierarchical cluster analysis of 61 families using three instruments and a questionnaire, in addition to weight, height, body fat percentage, hemoglobin A1c, total cholesterol, triglycerides, low-density lipoprotein and high-density lipoprotein. The analysis produced three groups of families. Wilk’s lambda demonstrated statistically significant differences provided by age (Λ = 0.778, F = 2.098, p = 0.010) and family health (Λ = 0.813, F = 2.650, p = 0.023). A post hoc Tukey test coincided with the three subsets. Families with type 2 diabetes have common elements that make them similar, while sharing differences that make them unique. PMID:27347419

  15. The formation of NGC 3603 young starburst cluster: "prompt" hierarchical assembly or monolithic starburst?

    CERN Document Server

    Banerjee, Sambaran

    2014-01-01

    The formation of very young massive clusters or "starburst" clusters is currently one of the most widely debated topic in astronomy. The classical notion dictates that a star cluster is formed in-situ in a dense molecular gas clump followed by a substantial residual gas expulsion. On the other hand, based on the observed morphologies of many young stellar associations, a hierarchical formation scenario is alternatively suggested. A very young (age $\\approx$ 1 Myr), massive ($>10^4M_\\odot$) star cluster like the Galactic NGC 3603 young cluster (HD 97950) is an appropriate testbed for distinguishing between such "monolithic" and "hierarchical" formation scenarios. A recent study by Banerjee and Kroupa (2014) demonstrates that the monolithic scenario remarkably reproduces the HD 97950 cluster. In the present work, we explore the possibility of the formation of the above cluster via hierarchical assembly of subclusters. These subclusters are initially distributed over a wide range of spatial volumes and have vari...

  16. Validation of hierarchical cluster analysis for identification of bacterial species using 42 bacterial isolates

    Science.gov (United States)

    Ghebremedhin, Meron; Yesupriya, Shubha; Luka, Janos; Crane, Nicole J.

    2015-03-01

    Recent studies have demonstrated the potential advantages of the use of Raman spectroscopy in the biomedical field due to its rapidity and noninvasive nature. In this study, Raman spectroscopy is applied as a method for differentiating between bacteria isolates for Gram status and Genus species. We created models for identifying 28 bacterial isolates using spectra collected with a 785 nm laser excitation Raman spectroscopic system. In order to investigate the groupings of these samples, partial least squares discriminant analysis (PLSDA) and hierarchical cluster analysis (HCA) was implemented. In addition, cluster analyses of the isolates were performed using various data types consisting of, biochemical tests, gene sequence alignment, high resolution melt (HRM) analysis and antimicrobial susceptibility tests of minimum inhibitory concentration (MIC) and degree of antimicrobial resistance (SIR). In order to evaluate the ability of these models to correctly classify bacterial isolates using solely Raman spectroscopic data, a set of 14 validation samples were tested using the PLSDA models and consequently the HCA models. External cluster evaluation criteria of purity and Rand index were calculated at different taxonomic levels to compare the performance of clustering using Raman spectra as well as the other datasets. Results showed that Raman spectra performed comparably, and in some cases better than, the other data types with Rand index and purity values up to 0.933 and 0.947, respectively. This study clearly demonstrates that the discrimination of bacterial species using Raman spectroscopic data and hierarchical cluster analysis is possible and has the potential to be a powerful point-of-care tool in clinical settings.

  17. A COMPARISON BETWEEN SINGLE LINKAGE AND COMPLETE LINKAGE IN AGGLOMERATIVE HIERARCHICAL CLUSTER ANALYSIS FOR IDENTIFYING TOURISTS SEGMENTS

    OpenAIRE

    Noor Rashidah Rashid

    2012-01-01

    Cluster Analysis is a multivariate method in statistics. Agglomerative Hierarchical Cluster Analysis is one of approaches in Cluster Analysis. There are two linkage methods in Agglomerative Hierarchical Cluster Analysis which are Single Linkage and Complete Linkage. The purpose of this study is to compare between Single Linkage and Complete Linkage in Agglomerative Hierarchical Cluster Analysis. The comparison of performances between these linkage methods was shown by using Kruskal-Wallis tes...

  18. A dynamic hierarchical clustering method for trajectory-based unusual video event detection.

    Science.gov (United States)

    Jiang, Fan; Wu, Ying; Katsaggelos, Aggelos K

    2009-04-01

    The proposed unusual video event detection method is based on unsupervised clustering of object trajectories, which are modeled by hidden Markov models (HMM). The novelty of the method includes a dynamic hierarchical process incorporated in the trajectory clustering algorithm to prevent model overfitting and a 2-depth greedy search strategy for efficient clustering.

  19. THE EVOLUTION OF BRIGHTEST CLUSTER GALAXIES IN A HIERARCHICAL UNIVERSE

    Energy Technology Data Exchange (ETDEWEB)

    Tonini, Chiara; Bernyk, Maksym; Croton, Darren [Centre for Astrophysics and Supercomputing, Swinburne University of Technology, Melbourne, VIC 3122 (Australia); Maraston, Claudia; Thomas, Daniel [Institute of Cosmology and Gravitation, University of Portsmouth, Portsmouth PO1 3FX (United Kingdom)

    2012-11-01

    We investigate the evolution of brightest cluster galaxies (BCGs) from redshift z {approx} 1.6 to z = 0. We upgrade the hierarchical semi-analytic model of Croton et al. with a new spectro-photometric model that produces realistic galaxy spectra, making use of the Maraston stellar populations and a new recipe for the dust extinction. We compare the model predictions of the K-band luminosity evolution and the J - K, V - I, and I - K color evolution with a series of data sets, including those of Collins et al. who argued that semi-analytic models based on the Millennium simulation cannot reproduce the red colors and high luminosity of BCGs at z > 1. We show instead that the model is well in range of the observed luminosity and correctly reproduces the color evolution of BCGs in the whole redshift range up to z {approx} 1.6. We argue that the success of the semi-analytic model is in large part due to the implementation of a more sophisticated spectro-photometric model. An analysis of the model BCGs shows an increase in mass by a factor of 2-3 since z {approx} 1, and star formation activity down to low redshifts. While the consensus regarding BCGs is that they are passively evolving, we argue that this conclusion is affected by the degeneracy between star formation history and stellar population models used in spectral energy distribution fitting, and by the inefficacy of toy models of passive evolution to capture the complexity of real galaxies, especially those with rich merger histories like BCGs. Following this argument, we also show that in the semi-analytic model the BCGs show a realistic mix of stellar populations, and that these stellar populations are mostly old. In addition, the age-redshift relation of the model BCGs follows that of the universe, meaning that given their merger history and star formation history, the ageing of BCGs is always dominated by the ageing of their stellar populations. In a {Lambda}CDM universe, we define such evolution as &apos

  20. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-07-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying Primary Biological Aerosol Particles (PBAP by applying hierarchical agglomerative cluster analysis to multi-parameter ultra violet-light induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1×106 points on a desktop computer, allowing for each fluorescent particle in a dataset to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient dataset. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best performing methods were applied to the BEACHON-RoMBAS ambient dataset where it was found that the z-score and range normalisation methods yield similar results with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the underestimation of bacterial aerosol concentration by a factor of 5. We suggest that this likely due to errors arising from misatrribution

  1. Evaluation of hierarchical agglomerative cluster analysis methods for discrimination of primary biological aerosol

    Directory of Open Access Journals (Sweden)

    I. Crawford

    2015-11-01

    Full Text Available In this paper we present improved methods for discriminating and quantifying primary biological aerosol particles (PBAPs by applying hierarchical agglomerative cluster analysis to multi-parameter ultraviolet-light-induced fluorescence (UV-LIF spectrometer data. The methods employed in this study can be applied to data sets in excess of 1 × 106 points on a desktop computer, allowing for each fluorescent particle in a data set to be explicitly clustered. This reduces the potential for misattribution found in subsampling and comparative attribution methods used in previous approaches, improving our capacity to discriminate and quantify PBAP meta-classes. We evaluate the performance of several hierarchical agglomerative cluster analysis linkages and data normalisation methods using laboratory samples of known particle types and an ambient data set. Fluorescent and non-fluorescent polystyrene latex spheres were sampled with a Wideband Integrated Bioaerosol Spectrometer (WIBS-4 where the optical size, asymmetry factor and fluorescent measurements were used as inputs to the analysis package. It was found that the Ward linkage with z-score or range normalisation performed best, correctly attributing 98 and 98.1 % of the data points respectively. The best-performing methods were applied to the BEACHON-RoMBAS (Bio–hydro–atmosphere interactions of Energy, Aerosols, Carbon, H2O, Organics and Nitrogen–Rocky Mountain Biogenic Aerosol Study ambient data set, where it was found that the z-score and range normalisation methods yield similar results, with each method producing clusters representative of fungal spores and bacterial aerosol, consistent with previous results. The z-score result was compared to clusters generated with previous approaches (WIBS AnalysiS Program, WASP where we observe that the subsampling and comparative attribution method employed by WASP results in the overestimation of the fungal spore concentration by a factor of 1.5 and the

  2. Modeling Hierarchically Clustered Longitudinal Survival Processes with Applications to Child Mortality and Maternal Health

    Directory of Open Access Journals (Sweden)

    Kuate-Defo, Bathélémy

    2001-01-01

    Full Text Available EnglishThis paper merges two parallel developments since the 1970s of newstatistical tools for data analysis: statistical methods known as hazard models that are used foranalyzing event-duration data and statistical methods for analyzing hierarchically clustered dataknown as multilevel models. These developments have rarely been integrated in research practice andthe formalization and estimation of models for hierarchically clustered survival data remain largelyuncharted. I attempt to fill some of this gap and demonstrate the merits of formulating and estimatingmultilevel hazard models with longitudinal data.FrenchCette étude intègre deux approches statistiques de pointe d'analyse des donnéesquantitatives depuis les années 70: les méthodes statistiques d'analyse desdonnées biographiques ou méthodes de survie et les méthodes statistiquesd'analyse des données hiérarchiques ou méthodes multi-niveaux. Ces deuxapproches ont été très peu mis en symbiose dans la pratique de recherche et parconséquent, la formulation et l'estimation des modèles appropriés aux donnéeslongitudinales et hiérarchiquement nichées demeure essentiellement un champd'investigation vierge. J'essaye de combler ce vide et j'utilise des données réellesen santé publique pour démontrer les mérites et contextes de formulation etd'estimation des modèles multi-niveaux et multi-états des données biographiqueset longitudinales.

  3. The Hierarchical Distribution of the Young Stellar Clusters in Six Local Star-forming Galaxies

    Science.gov (United States)

    Grasha, K.; Calzetti, D.; Adamo, A.; Kim, H.; Elmegreen, B. G.; Gouliermis, D. A.; Dale, D. A.; Fumagalli, M.; Grebel, E. K.; Johnson, K. E.; Kahre, L.; Kennicutt, R. C.; Messa, M.; Pellerin, A.; Ryon, J. E.; Smith, L. J.; Shabani, F.; Thilker, D.; Ubeda, L.

    2017-05-01

    We present a study of the hierarchical clustering of the young stellar clusters in six local (3-15 Mpc) star-forming galaxies using Hubble Space Telescope broadband WFC3/UVIS UV and optical images from the Treasury Program LEGUS (Legacy ExtraGalactic UV Survey). We identified 3685 likely clusters and associations, each visually classified by their morphology, and we use the angular two-point correlation function to study the clustering of these stellar systems. We find that the spatial distribution of the young clusters and associations are clustered with respect to each other, forming large, unbound hierarchical star-forming complexes that are in general very young. The strength of the clustering decreases with increasing age of the star clusters and stellar associations, becoming more homogeneously distributed after ˜40-60 Myr and on scales larger than a few hundred parsecs. In all galaxies, the associations exhibit a global behavior that is distinct and more strongly correlated from compact clusters. Thus, populations of clusters are more evolved than associations in terms of their spatial distribution, traveling significantly from their birth site within a few tens of Myr, whereas associations show evidence of disruption occurring very quickly after their formation. The clustering of the stellar systems resembles that of a turbulent interstellar medium that drives the star formation process, correlating the components in unbound star-forming complexes in a hierarchical manner, dispersing shortly after formation, suggestive of a single, continuous mode of star formation across all galaxies.

  4. 3D Nearest Neighbour Search Using a Clustered Hierarchical Tree Structure

    DEFF Research Database (Denmark)

    Suhaibah, A.; Uznir, U.; Antón Castro, Francesc/François

    2016-01-01

    , with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our...... findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results...... of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN) analysis. It uses a point location and identifies the surrounding neighbours. However...

  5. Content Based Image Retrieval using Hierarchical and K-Means Clustering Techniques

    Directory of Open Access Journals (Sweden)

    V.S.V.S. Murthy

    2010-03-01

    Full Text Available In this paper we present an image retrieval system that takes an image as the input query and retrieves images based on image content. Content Based Image Retrieval is an approach for retrieving semantically-relevant images from an image database based on automatically-derived image features. The unique aspect of the system is the utilization of hierarchical and k-means clustering techniques. The proposed procedure consists of two stages. First, here we are going to filter most of the images in the hierarchical clustering and then apply the clustered images to KMeans, so that we can get better favored image results.

  6. Hierarchical Control for Multiple DC-Microgrids Clusters

    DEFF Research Database (Denmark)

    Shafiee, Qobad; Dragicevic, Tomislav; Vasquez, Juan Carlos

    2014-01-01

    DC microgrids (MGs) have gained research interest during the recent years because of many potential advantages as compared to the ac system. To ensure reliable operation of a low-voltage dc MG as well as its intelligent operation with the other DC MGs, a hierarchical control is proposed in this p......DC microgrids (MGs) have gained research interest during the recent years because of many potential advantages as compared to the ac system. To ensure reliable operation of a low-voltage dc MG as well as its intelligent operation with the other DC MGs, a hierarchical control is proposed...

  7. Hierarchical Cluster Analysis: Comparison of Three Linkage Measures and Application to Psychological Data

    Directory of Open Access Journals (Sweden)

    Odilia Yim

    2015-02-01

    Full Text Available Cluster analysis refers to a class of data reduction methods used for sorting cases, observations, or variables of a given dataset into homogeneous groups that differ from each other. The present paper focuses on hierarchical agglomerative cluster analysis, a statistical technique where groups are sequentially created by systematically merging similar clusters together, as dictated by the distance and linkage measures chosen by the researcher. Specific distance and linkage measures are reviewed, including a discussion of how these choices can influence the clustering process by comparing three common linkage measures (single linkage, complete linkage, average linkage. The tutorial guides researchers in performing a hierarchical cluster analysis using the SPSS statistical software. Through an example, we demonstrate how cluster analysis can be used to detect meaningful subgroups in a sample of bilinguals by examining various language variables.

  8. Hierarchical cluster analysis of progression patterns in open-angle glaucoma patients with medical treatment.

    Science.gov (United States)

    Bae, Hyoung Won; Rho, Seungsoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

    2014-04-29

    To classify medically treated open-angle glaucoma (OAG) by the pattern of progression using hierarchical cluster analysis, and to determine OAG progression characteristics by comparing clusters. Ninety-five eyes of 95 OAG patients who received medical treatment, and who had undergone visual field (VF) testing at least once per year for 5 or more years. OAG was classified into subgroups using hierarchical cluster analysis based on the following five variables: baseline mean deviation (MD), baseline visual field index (VFI), MD slope, VFI slope, and Glaucoma Progression Analysis (GPA) printout. After that, other parameters were compared between clusters. Two clusters were made after a hierarchical cluster analysis. Cluster 1 showed -4.06 ± 2.43 dB baseline MD, 92.58% ± 6.27% baseline VFI, -0.28 ± 0.38 dB per year MD slope, -0.52% ± 0.81% per year VFI slope, and all "no progression" cases in GPA printout, whereas cluster 2 showed -8.68 ± 3.81 baseline MD, 77.54 ± 12.98 baseline VFI, -0.72 ± 0.55 MD slope, -2.22 ± 1.89 VFI slope, and seven "possible" and four "likely" progression cases in GPA printout. There were no significant differences in age, sex, mean IOP, central corneal thickness, and axial length between clusters. However, cluster 2 included more high-tension glaucoma patients and used a greater number of antiglaucoma eye drops significantly compared with cluster 1. Hierarchical cluster analysis of progression patterns divided OAG into slow and fast progression groups, evidenced by assessing the parameters of glaucomatous progression in VF testing. In the fast progression group, the prevalence of high-tension glaucoma was greater and the number of antiglaucoma medications administered was increased versus the slow progression group. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

  9. Periorbital melasma: Hierarchical cluster analysis of clinical features in Asian patients.

    Science.gov (United States)

    Jung, Y S; Bae, J M; Kim, B J; Kang, J-S; Cho, S B

    2017-03-19

    Studies have shown melasma lesions to be distributed across the face in centrofacial, malar, and mandibular patterns. Meanwhile, however, melasma lesions of the periorbital area have yet to be thoroughly described. We analyzed normal and ultraviolet light-exposed photographs of patients with melasma. The periorbital melasma lesions were measured according to anatomical reference points and a hierarchical cluster analysis was performed. The periorbital melasma lesions showed clinical features of fine and homogenous melasma pigmentation, involving both the upper and lower eyelids that extended to other anatomical sites with a darker and coarser appearance. The hierarchical cluster analysis indicated that patients with periorbital melasma can be categorized into two clusters according to the surface anatomy of the face. Significant differences between cluster 1 and cluster 2 were found in lateral distance and inferolateral distance, but not in medial distance and superior distance. Comparing the two clusters, patients in cluster 2 were found to be significantly older and more commonly accompanied by melasma lesions of the temple and medial cheek. Our hierarchical cluster analysis of periorbital melasma lesions demonstrated that Asian patients with periorbital melasma can be categorized into two clusters according to the surface anatomy of the face. © 2017 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

  10. Evaluation by hierarchical clustering of multiple cytokine expression after phytohemagglutinin stimulation

    Directory of Open Access Journals (Sweden)

    Yang Chunhe

    2016-01-01

    Full Text Available The hierarchical clustering method has been used for exploration of gene expression and proteomic profiles; however, little research into its application in the examination of expression of multiplecytokine/chemokine responses to stimuli has been reported. Thus, little progress has been made on how phytohemagglutinin(PHA affects cytokine expression profiling on a large scale in the human hematological system. To investigate the characteristic expression pattern under PHA stimulation, Luminex, a multiplex bead-based suspension array, was performed. The data set collected from human peripheral blood mononuclear cells (PBMC was analyzed using the hierarchical clustering method. It was revealed that two specific chemokines (CCL3 andCCL4 underwent significantly greater quantitative changes during induction of expression than other tested cytokines/chemokines after PHA stimulation. This result indicates that hierarchical clustering is a useful tool for detecting fine patterns during exploration of biological data, and that it can play an important role in comparative studies.

  11. Hierarchical trie packet classification algorithm based on expectation-maximization clustering

    Science.gov (United States)

    Bi, Xia-an; Zhao, Junxia

    2017-01-01

    With the development of computer network bandwidth, packet classification algorithms which are able to deal with large-scale rule sets are in urgent need. Among the existing algorithms, researches on packet classification algorithms based on hierarchical trie have become an important packet classification research branch because of their widely practical use. Although hierarchical trie is beneficial to save large storage space, it has several shortcomings such as the existence of backtracking and empty nodes. This paper proposes a new packet classification algorithm, Hierarchical Trie Algorithm Based on Expectation-Maximization Clustering (HTEMC). Firstly, this paper uses the formalization method to deal with the packet classification problem by means of mapping the rules and data packets into a two-dimensional space. Secondly, this paper uses expectation-maximization algorithm to cluster the rules based on their aggregate characteristics, and thereby diversified clusters are formed. Thirdly, this paper proposes a hierarchical trie based on the results of expectation-maximization clustering. Finally, this paper respectively conducts simulation experiments and real-environment experiments to compare the performances of our algorithm with other typical algorithms, and analyzes the results of the experiments. The hierarchical trie structure in our algorithm not only adopts trie path compression to eliminate backtracking, but also solves the problem of low efficiency of trie updates, which greatly improves the performance of the algorithm. PMID:28704476

  12. The Evolution of Galaxy Clustering in Hierarchical Models

    OpenAIRE

    1999-01-01

    The main ingredients of recent semi-analytic models of galaxy formation are summarised. We present predictions for the galaxy clustering properties of a well specified LCDM model whose parameters are constrained by observed local galaxy properties. We present preliminary predictions for evolution of clustering that can be probed with deep pencil beam surveys.

  13. Clinical fracture risk evaluated by hierarchical agglomerative clustering

    DEFF Research Database (Denmark)

    Kruse, Christian; Eiken, P; Vestergaard, P

    2017-01-01

    profiles. INTRODUCTION: The purposes of this study were to establish and quantify patient clusters of high, average and low fracture risk using an unsupervised machine learning algorithm. METHODS: Regional and national Danish patient data on dual-energy X-ray absorptiometry (DXA) scans, medication...... containing less than 250 subjects. Clusters were identified as high, average or low fracture risk based on bone mineral density (BMD) characteristics. Cluster-based descriptive statistics and relative Z-scores for variable means were computed. RESULTS: Ten thousand seven hundred seventy-five women were...... as low fracture risk with high to very high BMD. A mean age of 60 years was the earliest that allowed for separation of high-risk clusters. DXA scan results could identify high-risk subjects with different antiresorptive treatment compliance levels based on similarities and differences in lumbar spine...

  14. Mapping informative clusters in a hierarchical [corrected] framework of FMRI multivariate analysis.

    Directory of Open Access Journals (Sweden)

    Rui Xu

    Full Text Available Pattern recognition methods have become increasingly popular in fMRI data analysis, which are powerful in discriminating between multi-voxel patterns of brain activities associated with different mental states. However, when they are used in functional brain mapping, the location of discriminative voxels varies significantly, raising difficulties in interpreting the locus of the effect. Here we proposed a hierarchical framework of multivariate approach that maps informative clusters rather than voxels to achieve reliable functional brain mapping without compromising the discriminative power. In particular, we first searched for local homogeneous clusters that consisted of voxels with similar response profiles. Then, a multi-voxel classifier was built for each cluster to extract discriminative information from the multi-voxel patterns. Finally, through multivariate ranking, outputs from the classifiers were served as a multi-cluster pattern to identify informative clusters by examining interactions among clusters. Results from both simulated and real fMRI data demonstrated that this hierarchical approach showed better performance in the robustness of functional brain mapping than traditional voxel-based multivariate methods. In addition, the mapped clusters were highly overlapped for two perceptually equivalent object categories, further confirming the validity of our approach. In short, the hierarchical framework of multivariate approach is suitable for both pattern classification and brain mapping in fMRI studies.

  15. Kendall’s tau and agglomerative clustering for structure determination of hierarchical Archimedean copulas

    Directory of Open Access Journals (Sweden)

    Górecki J.

    2017-01-01

    Full Text Available Several successful approaches to structure determination of hierarchical Archimedean copulas (HACs proposed in the literature rely on agglomerative clustering and Kendall’s correlation coefficient. However, there has not been presented any theoretical proof justifying such approaches. This work fills this gap and introduces a theorem showing that, given the matrix of the pairwise Kendall correlation coefficients corresponding to a HAC, its structure can be recovered by an agglomerative clustering technique.

  16. Hierarchical cluster-tendency analysis of the group structure in the foreign exchange market

    Science.gov (United States)

    Wu, Xin-Ye; Zheng, Zhi-Gang

    2013-08-01

    A hierarchical cluster-tendency (HCT) method in analyzing the group structure of networks of the global foreign exchange (FX) market is proposed by combining the advantages of both the minimal spanning tree (MST) and the hierarchical tree (HT). Fifty currencies of the top 50 World GDP in 2010 according to World Bank's database are chosen as the underlying system. By using the HCT method, all nodes in the FX market network can be "colored" and distinguished. We reveal that the FX networks can be divided into two groups, i.e., the Asia-Pacific group and the Pan-European group. The results given by the hierarchical cluster-tendency method agree well with the formerly observed geographical aggregation behavior in the FX market. Moreover, an oil-resource aggregation phenomenon is discovered by using our method. We find that gold could be a better numeraire for the weekly-frequency FX data.

  17. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering

    Science.gov (United States)

    In this study, hierarchical clustering classification models were developed to predict in vitro and in vivo oestrogen receptor (ER) activity. Classification models were developed for binding, agonist, and antagonist in vitro ER activity and for mouse in vivo uterotrophic ER bindi...

  18. Prediction of in vitro and in vivo oestrogen receptor activity using hierarchical clustering

    Science.gov (United States)

    In this study, hierarchical clustering classification models were developed to predict in vitro and in vivo oestrogen receptor (ER) activity. Classification models were developed for binding, agonist, and antagonist in vitro ER activity and for mouse in vivo uterotrophic ER bindi...

  19. Analysis of genomic signatures in prokaryotes using multinomial regression and hierarchical clustering

    DEFF Research Database (Denmark)

    Ussery, David; Bohlin, Jon; Skjerve, Eystein

    2009-01-01

    Recently there has been an explosion in the availability of bacterial genomic sequences, making possible now an analysis of genomic signatures across more than 800 hundred different bacterial chromosomes, from a wide variety of environments. Using genomic signatures, we pair-wise compared 867...... different genomic DNA sequences, taken from chromosomes and plasmids more than 100,000 base-pairs in length. Hierarchical clustering was performed on the outcome of the comparisons before a multinomial regression model was fitted. The regression model included the cluster groups as the response variable...... AT content. Small improvements to the regression model, although significant, were also obtained by factors such as sequence size, habitat, growth temperature, selective pressure measured as oligonucleotide usage variance, and oxygen requirement.The statistics obtained using hierarchical clustering...

  20. Signatures of Hierarchical Clustering in Dark Matter Detection Experiments

    CERN Document Server

    Stiff, D; Frieman, Joshua A

    2001-01-01

    In the cold dark matter model of structure formation, galaxies are assembled hierarchically from mergers and the accretion of subclumps. This process is expected to leave residual substructure in the Galactic dark halo, including partially disrupted clumps and their associated tidal debris. We develop a model for such halo substructure and study its implications for dark matter (WIMP and axion) detection experiments. We combine the Press-Schechter model for the distribution of halo subclump masses with N-body simulations of the evolution and disruption of individual clumps as they orbit through the evolving Galaxy to derive the probability that the Earth is passing through a subclump or stream of a given density. Our results suggest that it is likely that the local complement of dark matter particles includes a 1-5% contribution from a single clump. The implications for dark matter detection experiments are significant, since the disrupted clump is composed of a `cold' flow of high-velocity particles. We desc...

  1. Clustering dynamic textures with the hierarchical em algorithm for modeling video.

    Science.gov (United States)

    Mumtaz, Adeel; Coviello, Emanuele; Lanckriet, Gert R G; Chan, Antoni B

    2013-07-01

    Dynamic texture (DT) is a probabilistic generative model, defined over space and time, that represents a video as the output of a linear dynamical system (LDS). The DT model has been applied to a wide variety of computer vision problems, such as motion segmentation, motion classification, and video registration. In this paper, we derive a new algorithm for clustering DT models that is based on the hierarchical EM algorithm. The proposed clustering algorithm is capable of both clustering DTs and learning novel DT cluster centers that are representative of the cluster members in a manner that is consistent with the underlying generative probabilistic model of the DT. We also derive an efficient recursive algorithm for sensitivity analysis of the discrete-time Kalman smoothing filter, which is used as the basis for computing expectations in the E-step of the HEM algorithm. Finally, we demonstrate the efficacy of the clustering algorithm on several applications in motion analysis, including hierarchical motion clustering, semantic motion annotation, and learning bag-of-systems (BoS) codebooks for dynamic texture recognition.

  2. Improving the Decision Value of Hierarchical Text Clustering Using Term Overlap Detection

    Directory of Open Access Journals (Sweden)

    Nilupulee Nathawitharana

    2015-09-01

    Full Text Available Humans are used to expressing themselves with written language and language provides a medium with which we can describe our experiences in detail incorporating individuality. Even though documents provide a rich source of information, it becomes very difficult to identify, extract, summarize and search when vast amounts of documents are collected especially over time. Document clustering is a technique that has been widely used to group documents based on similarity of content represented by the words used. Once key groups are identified further drill down into sub-groupings is facilitated by the use of hierarchical clustering. Clustering and hierarchical clustering are very useful when applied to numerical and categorical data and cluster accuracy and purity measures exist to evaluate the outcomes of a clustering exercise. Although the same measures have been applied to text clustering, text clusters are based on words or terms which can be repeated across documents associated with different topics. Therefore text data cannot be considered as a direct ‘coding’ of a particular experience or situation in contrast to numerical and categorical data and term overlap is a very common characteristic in text clustering. In this paper we propose a new technique and methodology for term overlap capture from text documents, highlighting the different situations such overlap could signify and discuss why such understanding is important for obtaining value from text clustering. Experiments were conducted using a widely used text document collection where the proposed methodology allowed exploring the term diversity for a given document collection and obtain clusters with minimum term overlap.

  3. 3D NEAREST NEIGHBOUR SEARCH USING A CLUSTERED HIERARCHICAL TREE STRUCTURE

    Directory of Open Access Journals (Sweden)

    A. Suhaibah

    2016-06-01

    Full Text Available Locating and analysing the location of new stores or outlets is one of the common issues facing retailers and franchisers. This is due to assure that new opening stores are at their strategic location to attract the highest possible number of customers. Spatial information is used to manage, maintain and analyse these store locations. However, since the business of franchising and chain stores in urban areas runs within high rise multi-level buildings, a three-dimensional (3D method is prominently required in order to locate and identify the surrounding information such as at which level of the franchise unit will be located or is the franchise unit located is at the best level for visibility purposes. One of the common used analyses used for retrieving the surrounding information is Nearest Neighbour (NN analysis. It uses a point location and identifies the surrounding neighbours. However, with the immense number of urban datasets, the retrieval and analysis of nearest neighbour information and their efficiency will become more complex and crucial. In this paper, we present a technique to retrieve nearest neighbour information in 3D space using a clustered hierarchical tree structure. Based on our findings, the proposed approach substantially showed an improvement of response time analysis compared to existing approaches of spatial access methods in databases. The query performance was tested using a dataset consisting of 500,000 point locations building and franchising unit. The results are presented in this paper. Another advantage of this structure is that it also offers a minimal overlap and coverage among nodes which can reduce repetitive data entry.

  4. Multilevel hierarchical kernel spectral clustering for real-life large scale complex networks.

    Directory of Open Access Journals (Sweden)

    Raghvendra Mall

    Full Text Available Kernel spectral clustering corresponds to a weighted kernel principal component analysis problem in a constrained optimization framework. The primal formulation leads to an eigen-decomposition of a centered Laplacian matrix at the dual level. The dual formulation allows to build a model on a representative subgraph of the large scale network in the training phase and the model parameters are estimated in the validation stage. The KSC model has a powerful out-of-sample extension property which allows cluster affiliation for the unseen nodes of the big data network. In this paper we exploit the structure of the projections in the eigenspace during the validation stage to automatically determine a set of increasing distance thresholds. We use these distance thresholds in the test phase to obtain multiple levels of hierarchy for the large scale network. The hierarchical structure in the network is determined in a bottom-up fashion. We empirically showcase that real-world networks have multilevel hierarchical organization which cannot be detected efficiently by several state-of-the-art large scale hierarchical community detection techniques like the Louvain, OSLOM and Infomap methods. We show that a major advantage of our proposed approach is the ability to locate good quality clusters at both the finer and coarser levels of hierarchy using internal cluster quality metrics on 7 real-life networks.

  5. HCsnip: An R Package for Semi-supervised Snipping of the Hierarchical Clustering Tree.

    Science.gov (United States)

    Obulkasim, Askar; van de Wiel, Mark A

    2015-01-01

    Hierarchical clustering (HC) is one of the most frequently used methods in computational biology in the analysis of high-dimensional genomics data. Given a data set, HC outputs a binary tree leaves of which are the data points and internal nodes represent clusters of various sizes. Normally, a fixed-height cut on the HC tree is chosen, and each contiguous branch of data points below that height is considered as a separate cluster. However, the fixed-height branch cut may not be ideal in situations where one expects a complicated tree structure with nested clusters. Furthermore, due to lack of utilization of related background information in selecting the cutoff, induced clusters are often difficult to interpret. This paper describes a novel procedure that aims to automatically extract meaningful clusters from the HC tree in a semi-supervised way. The procedure is implemented in the R package HCsnip available from Bioconductor. Rather than cutting the HC tree at a fixed-height, HCsnip probes the various way of snipping, possibly at variable heights, to tease out hidden clusters ensconced deep down in the tree. The cluster extraction process utilizes, along with the data set from which the HC tree is derived, commonly available background information. Consequently, the extracted clusters are highly reproducible and robust against various sources of variations that "haunted" high-dimensional genomics data. Since the clustering process is guided by the background information, clusters are easy to interpret. Unlike existing packages, no constraint is placed on the data type on which clustering is desired. Particularly, the package accepts patient follow-up data for guiding the cluster extraction process. To our knowledge, HCsnip is the first package that is able to decomposes the HC tree into clusters with piecewise snipping under the guidance of patient time-to-event information. Our implementation of the semi-supervised HC tree snipping framework is generic, and can

  6. A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables.

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    Guillaume Marrelec

    Full Text Available The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity, provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering.

  7. A Bayesian Alternative to Mutual Information for the Hierarchical Clustering of Dependent Random Variables.

    Science.gov (United States)

    Marrelec, Guillaume; Messé, Arnaud; Bellec, Pierre

    2015-01-01

    The use of mutual information as a similarity measure in agglomerative hierarchical clustering (AHC) raises an important issue: some correction needs to be applied for the dimensionality of variables. In this work, we formulate the decision of merging dependent multivariate normal variables in an AHC procedure as a Bayesian model comparison. We found that the Bayesian formulation naturally shrinks the empirical covariance matrix towards a matrix set a priori (e.g., the identity), provides an automated stopping rule, and corrects for dimensionality using a term that scales up the measure as a function of the dimensionality of the variables. Also, the resulting log Bayes factor is asymptotically proportional to the plug-in estimate of mutual information, with an additive correction for dimensionality in agreement with the Bayesian information criterion. We investigated the behavior of these Bayesian alternatives (in exact and asymptotic forms) to mutual information on simulated and real data. An encouraging result was first derived on simulations: the hierarchical clustering based on the log Bayes factor outperformed off-the-shelf clustering techniques as well as raw and normalized mutual information in terms of classification accuracy. On a toy example, we found that the Bayesian approaches led to results that were similar to those of mutual information clustering techniques, with the advantage of an automated thresholding. On real functional magnetic resonance imaging (fMRI) datasets measuring brain activity, it identified clusters consistent with the established outcome of standard procedures. On this application, normalized mutual information had a highly atypical behavior, in the sense that it systematically favored very large clusters. These initial experiments suggest that the proposed Bayesian alternatives to mutual information are a useful new tool for hierarchical clustering.

  8. Teaching a machine to see: unsupervised image segmentation and categorisation using growing neural gas and hierarchical clustering

    CERN Document Server

    Hocking, Alex; Davey, Neil; Sun, Yi

    2015-01-01

    We present a novel unsupervised learning approach to automatically segment and label images in astronomical surveys. Automation of this procedure will be essential as next-generation surveys enter the petabyte scale: data volumes will exceed the capability of even large crowd-sourced analyses. We demonstrate how a growing neural gas (GNG) can be used to encode the feature space of imaging data. When coupled with a technique called hierarchical clustering, imaging data can be automatically segmented and labelled by organising nodes in the GNG. The key distinction of unsupervised learning is that these labels need not be known prior to training, rather they are determined by the algorithm itself. Importantly, after training a network can be be presented with images it has never 'seen' before and provide consistent categorisation of features. As a proof-of-concept we demonstrate application on data from the Hubble Space Telescope Frontier Fields: images of clusters of galaxies containing a mixture of galaxy type...

  9. The evolution of Brightest Cluster Galaxies in a hierarchical universe

    CERN Document Server

    Tonini, Chiara; Croton, Darren; Maraston, Claudia; Thomas, Daniel

    2012-01-01

    We investigate the evolution of Brightest Cluster Galaxies (BCGs) from redshift z~1.6 to z~0. We use the semi-analytic model of Croton et al. (2006) with a new spectro-photometric model based on the Maraston (2005) stellar populations and a new recipe for the dust extinction. We compare the model predictions of the K-band luminosity evolution and the J-K, V-I and I-K colour evolution with a series of datasets, including Collins et al. (Nature, 2009) who argued that semi-analytic models based on the Millennium simulation cannot reproduce the red colours and high luminosity of BCGs at z>1. We show instead that the model is well in range of the observed luminosity and correctly reproduces the colour evolution of BCGs in the whole redshift range up to z~1.6. We argue that the success of the semi-analytic model is in large part due to the implementation of a more sophisticated spectro-photometric model. An analysis of the model BCGs shows an increase in mass by a factor ~2 since z~1, and star formation activity do...

  10. Bayesian latent variable models for hierarchical clustered count outcomes with repeated measures in microbiome studies.

    Science.gov (United States)

    Xu, Lizhen; Paterson, Andrew D; Xu, Wei

    2017-04-01

    Motivated by the multivariate nature of microbiome data with hierarchical taxonomic clusters, counts that are often skewed and zero inflated, and repeated measures, we propose a Bayesian latent variable methodology to jointly model multiple operational taxonomic units within a single taxonomic cluster. This novel method can incorporate both negative binomial and zero-inflated negative binomial responses, and can account for serial and familial correlations. We develop a Markov chain Monte Carlo algorithm that is built on a data augmentation scheme using Pólya-Gamma random variables. Hierarchical centering and parameter expansion techniques are also used to improve the convergence of the Markov chain. We evaluate the performance of our proposed method through extensive simulations. We also apply our method to a human microbiome study.

  11. Bayesian hierarchical clustering for studying cancer gene expression data with unknown statistics.

    Directory of Open Access Journals (Sweden)

    Korsuk Sirinukunwattana

    Full Text Available Clustering analysis is an important tool in studying gene expression data. The Bayesian hierarchical clustering (BHC algorithm can automatically infer the number of clusters and uses Bayesian model selection to improve clustering quality. In this paper, we present an extension of the BHC algorithm. Our Gaussian BHC (GBHC algorithm represents data as a mixture of Gaussian distributions. It uses normal-gamma distribution as a conjugate prior on the mean and precision of each of the Gaussian components. We tested GBHC over 11 cancer and 3 synthetic datasets. The results on cancer datasets show that in sample clustering, GBHC on average produces a clustering partition that is more concordant with the ground truth than those obtained from other commonly used algorithms. Furthermore, GBHC frequently infers the number of clusters that is often close to the ground truth. In gene clustering, GBHC also produces a clustering partition that is more biologically plausible than several other state-of-the-art methods. This suggests GBHC as an alternative tool for studying gene expression data. The implementation of GBHC is available at https://sites.google.com/site/gaussianbhc/

  12. Hierarchical and Non-Hierarchical Linear and Non-Linear Clustering Methods to “Shakespeare Authorship Question”

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    Refat Aljumily

    2015-09-01

    Full Text Available A few literary scholars have long claimed that Shakespeare did not write some of his best plays (history plays and tragedies and proposed at one time or another various suspect authorship candidates. Most modern-day scholars of Shakespeare have rejected this claim, arguing that strong evidence that Shakespeare wrote the plays and poems being his name appears on them as the author. This has caused and led to an ongoing scholarly academic debate for quite some long time. Stylometry is a fast-growing field often used to attribute authorship to anonymous or disputed texts. Stylometric attempts to resolve this literary puzzle have raised interesting questions over the past few years. The following paper contributes to “the Shakespeare authorship question” by using a mathematically-based methodology to examine the hypothesis that Shakespeare wrote all the disputed plays traditionally attributed to him. More specifically, the mathematically based methodology used here is based on Mean Proximity, as a linear hierarchical clustering method, and on Principal Components Analysis, as a non-hierarchical linear clustering method. It is also based, for the first time in the domain, on Self-Organizing Map U-Matrix and Voronoi Map, as non-linear clustering methods to cover the possibility that our data contains significant non-linearities. Vector Space Model (VSM is used to convert texts into vectors in a high dimensional space. The aim of which is to compare the degrees of similarity within and between limited samples of text (the disputed plays. The various works and plays assumed to have been written by Shakespeare and possible authors notably, Sir Francis Bacon, Christopher Marlowe, John Fletcher, and Thomas Kyd, where “similarity” is defined in terms of correlation/distance coefficient measure based on the frequency of usage profiles of function words, word bi-grams, and character triple-grams. The claim that Shakespeare authored all the disputed

  13. An Exactly Soluble Hierarchical Clustering Model Inverse Cascades, Self-Similarity, and Scaling

    CERN Document Server

    Gabrielov, A; Turcotte, D L

    1999-01-01

    We show how clustering as a general hierarchical dynamical process proceeds via a sequence of inverse cascades to produce self-similar scaling, as an intermediate asymptotic, which then truncates at the largest spatial scales. We show how this model can provide a general explanation for the behavior of several models that has been described as ``self-organized critical,'' including forest-fire, sandpile, and slider-block models.

  14. Recursive Hierarchical Image Segmentation by Region Growing and Constrained Spectral Clustering

    Science.gov (United States)

    Tilton, James C.

    2002-01-01

    This paper describes an algorithm for hierarchical image segmentation (referred to as HSEG) and its recursive formulation (referred to as RHSEG). The HSEG algorithm is a hybrid of region growing and constrained spectral clustering that produces a hierarchical set of image segmentations based on detected convergence points. In the main, HSEG employs the hierarchical stepwise optimization (HS WO) approach to region growing, which seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing. In addition, HSEG optionally interjects between HSWO region growing iterations merges between spatially non-adjacent regions (i.e., spectrally based merging or clustering) constrained by a threshold derived from the previous HSWO region growing iteration. While the addition of constrained spectral clustering improves the segmentation results, especially for larger images, it also significantly increases HSEG's computational requirements. To counteract this, a computationally efficient recursive, divide-and-conquer, implementation of HSEG (RHSEG) has been devised and is described herein. Included in this description is special code that is required to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. Implementations for single processor and for multiple processor computer systems are described. Results with Landsat TM data are included comparing HSEG with classic region growing. Finally, an application to image information mining and knowledge discovery is discussed.

  15. Hierarchical Adaptive Means (HAM) clustering for hardware-efficient, unsupervised and real-time spike sorting.

    Science.gov (United States)

    Paraskevopoulou, Sivylla E; Wu, Di; Eftekhar, Amir; Constandinou, Timothy G

    2014-09-30

    This work presents a novel unsupervised algorithm for real-time adaptive clustering of neural spike data (spike sorting). The proposed Hierarchical Adaptive Means (HAM) clustering method combines centroid-based clustering with hierarchical cluster connectivity to classify incoming spikes using groups of clusters. It is described how the proposed method can adaptively track the incoming spike data without requiring any past history, iteration or training and autonomously determines the number of spike classes. Its performance (classification accuracy) has been tested using multiple datasets (both simulated and recorded) achieving a near-identical accuracy compared to k-means (using 10-iterations and provided with the number of spike classes). Also, its robustness in applying to different feature extraction methods has been demonstrated by achieving classification accuracies above 80% across multiple datasets. Last but crucially, its low complexity, that has been quantified through both memory and computation requirements makes this method hugely attractive for future hardware implementation. Copyright © 2014 Elsevier B.V. All rights reserved.

  16. An energy efficient cooperative hierarchical MIMO clustering scheme for wireless sensor networks.

    Science.gov (United States)

    Nasim, Mehwish; Qaisar, Saad; Lee, Sungyoung

    2012-01-01

    In this work, we present an energy efficient hierarchical cooperative clustering scheme for wireless sensor networks. Communication cost is a crucial factor in depleting the energy of sensor nodes. In the proposed scheme, nodes cooperate to form clusters at each level of network hierarchy ensuring maximal coverage and minimal energy expenditure with relatively uniform distribution of load within the network. Performance is enhanced by cooperative multiple-input multiple-output (MIMO) communication ensuring energy efficiency for WSN deployments over large geographical areas. We test our scheme using TOSSIM and compare the proposed scheme with cooperative multiple-input multiple-output (CMIMO) clustering scheme and traditional multihop Single-Input-Single-Output (SISO) routing approach. Performance is evaluated on the basis of number of clusters, number of hops, energy consumption and network lifetime. Experimental results show significant energy conservation and increase in network lifetime as compared to existing schemes.

  17. To Aggregate or Not and Potentially Better Questions for Clustered Data: The Need for Hierarchical Linear Modeling in CTE Research

    Science.gov (United States)

    Nimon, Kim

    2012-01-01

    Using state achievement data that are openly accessible, this paper demonstrates the application of hierarchical linear modeling within the context of career technical education research. Three prominent approaches to analyzing clustered data (i.e., modeling aggregated data, modeling disaggregated data, modeling hierarchical data) are discussed…

  18. Novel density-based and hierarchical density-based clustering algorithms for uncertain data.

    Science.gov (United States)

    Zhang, Xianchao; Liu, Han; Zhang, Xiaotong

    2017-09-01

    Uncertain data has posed a great challenge to traditional clustering algorithms. Recently, several algorithms have been proposed for clustering uncertain data, and among them density-based techniques seem promising for handling data uncertainty. However, some issues like losing uncertain information, high time complexity and nonadaptive threshold have not been addressed well in the previous density-based algorithm FDBSCAN and hierarchical density-based algorithm FOPTICS. In this paper, we firstly propose a novel density-based algorithm PDBSCAN, which improves the previous FDBSCAN from the following aspects: (1) it employs a more accurate method to compute the probability that the distance between two uncertain objects is less than or equal to a boundary value, instead of the sampling-based method in FDBSCAN; (2) it introduces new definitions of probability neighborhood, support degree, core object probability, direct reachability probability, thus reducing the complexity and solving the issue of nonadaptive threshold (for core object judgement) in FDBSCAN. Then, we modify the algorithm PDBSCAN to an improved version (PDBSCANi), by using a better cluster assignment strategy to ensure that every object will be assigned to the most appropriate cluster, thus solving the issue of nonadaptive threshold (for direct density reachability judgement) in FDBSCAN. Furthermore, as PDBSCAN and PDBSCANi have difficulties for clustering uncertain data with non-uniform cluster density, we propose a novel hierarchical density-based algorithm POPTICS by extending the definitions of PDBSCAN, adding new definitions of fuzzy core distance and fuzzy reachability distance, and employing a new clustering framework. POPTICS can reveal the cluster structures of the datasets with different local densities in different regions better than PDBSCAN and PDBSCANi, and it addresses the issues in FOPTICS. Experimental results demonstrate the superiority of our proposed algorithms over the existing

  19. Determination of genetic structure of germplasm collections: are traditional hierarchical clustering methods appropriate for molecular marker data?

    Science.gov (United States)

    Odong, T L; van Heerwaarden, J; Jansen, J; van Hintum, T J L; van Eeuwijk, F A

    2011-07-01

    Despite the availability of newer approaches, traditional hierarchical clustering remains very popular in genetic diversity studies in plants. However, little is known about its suitability for molecular marker data. We studied the performance of traditional hierarchical clustering techniques using real and simulated molecular marker data. Our study also compared the performance of traditional hierarchical clustering with model-based clustering (STRUCTURE). We showed that the cophenetic correlation coefficient is directly related to subgroup differentiation and can thus be used as an indicator of the presence of genetically distinct subgroups in germplasm collections. Whereas UPGMA performed well in preserving distances between accessions, Ward excelled in recovering groups. Our results also showed a close similarity between clusters obtained by Ward and by STRUCTURE. Traditional cluster analysis can provide an easy and effective way of determining structure in germplasm collections using molecular marker data, and, the output can be used for sampling core collections or for association studies.

  20. A comparison of hierarchical cluster analysis and league table rankings as methods for analysis and presentation of district health system performance data in Uganda.

    Science.gov (United States)

    Tashobya, Christine K; Dubourg, Dominique; Ssengooba, Freddie; Speybroeck, Niko; Macq, Jean; Criel, Bart

    2016-03-01

    In 2003, the Uganda Ministry of Health introduced the district league table for district health system performance assessment. The league table presents district performance against a number of input, process and output indicators and a composite index to rank districts. This study explores the use of hierarchical cluster analysis for analysing and presenting district health systems performance data and compares this approach with the use of the league table in Uganda. Ministry of Health and district plans and reports, and published documents were used to provide information on the development and utilization of the Uganda district league table. Quantitative data were accessed from the Ministry of Health databases. Statistical analysis using SPSS version 20 and hierarchical cluster analysis, utilizing Wards' method was used. The hierarchical cluster analysis was conducted on the basis of seven clusters determined for each year from 2003 to 2010, ranging from a cluster of good through moderate-to-poor performers. The characteristics and membership of clusters varied from year to year and were determined by the identity and magnitude of performance of the individual variables. Criticisms of the league table include: perceived unfairness, as it did not take into consideration district peculiarities; and being oversummarized and not adequately informative. Clustering organizes the many data points into clusters of similar entities according to an agreed set of indicators and can provide the beginning point for identifying factors behind the observed performance of districts. Although league table ranking emphasize summation and external control, clustering has the potential to encourage a formative, learning approach. More research is required to shed more light on factors behind observed performance of the different clusters. Other countries especially low-income countries that share many similarities with Uganda can learn from these experiences. © The Author 2015

  1. Applying of hierarchical clustering to analysis of protein patterns in the human cancer-associated liver.

    Directory of Open Access Journals (Sweden)

    Natalia A Petushkova

    Full Text Available There are two ways that statistical methods can learn from biomedical data. One way is to learn classifiers to identify diseases and to predict outcomes using the training dataset with established diagnosis for each sample. When the training dataset is not available the task can be to mine for presence of meaningful groups (clusters of samples and to explore underlying data structure (unsupervised learning.We investigated the proteomic profiles of the cytosolic fraction of human liver samples using two-dimensional electrophoresis (2DE. Samples were resected upon surgical treatment of hepatic metastases in colorectal cancer. Unsupervised hierarchical clustering of 2DE gel images (n = 18 revealed a pair of clusters, containing 11 and 7 samples. Previously we used the same specimens to measure biochemical profiles based on cytochrome P450-dependent enzymatic activities and also found that samples were clearly divided into two well-separated groups by cluster analysis. It turned out that groups by enzyme activity almost perfectly match to the groups identified from proteomic data. Of the 271 reproducible spots on our 2DE gels, we selected 15 to distinguish the human liver cytosolic clusters. Using MALDI-TOF peptide mass fingerprinting, we identified 12 proteins for the selected spots, including known cancer-associated species.Our results highlight the importance of hierarchical cluster analysis of proteomic data, and showed concordance between results of biochemical and proteomic approaches. Grouping of the human liver samples and/or patients into differing clusters may provide insights into possible molecular mechanism of drug metabolism and creates a rationale for personalized treatment.

  2. SHIPS: Spectral Hierarchical clustering for the Inference of Population Structure in genetic studies.

    Science.gov (United States)

    Bouaziz, Matthieu; Paccard, Caroline; Guedj, Mickael; Ambroise, Christophe

    2012-01-01

    Inferring the structure of populations has many applications for genetic research. In addition to providing information for evolutionary studies, it can be used to account for the bias induced by population stratification in association studies. To this end, many algorithms have been proposed to cluster individuals into genetically homogeneous sub-populations. The parametric algorithms, such as Structure, are very popular but their underlying complexity and their high computational cost led to the development of faster parametric alternatives such as Admixture. Alternatives to these methods are the non-parametric approaches. Among this category, AWclust has proven efficient but fails to properly identify population structure for complex datasets. We present in this article a new clustering algorithm called Spectral Hierarchical clustering for the Inference of Population Structure (SHIPS), based on a divisive hierarchical clustering strategy, allowing a progressive investigation of population structure. This method takes genetic data as input to cluster individuals into homogeneous sub-populations and with the use of the gap statistic estimates the optimal number of such sub-populations. SHIPS was applied to a set of simulated discrete and admixed datasets and to real SNP datasets, that are data from the HapMap and Pan-Asian SNP consortium. The programs Structure, Admixture, AWclust and PCAclust were also investigated in a comparison study. SHIPS and the parametric approach Structure were the most accurate when applied to simulated datasets both in terms of individual assignments and estimation of the correct number of clusters. The analysis of the results on the real datasets highlighted that the clusterings of SHIPS were the more consistent with the population labels or those produced by the Admixture program. The performances of SHIPS when applied to SNP data, along with its relatively low computational cost and its ease of use make this method a promising

  3. Evolutionary-Hierarchical Bases of the Formation of Cluster Model of Innovation Economic Development

    Directory of Open Access Journals (Sweden)

    Yuliya Vladimirovna Dubrovskaya

    2016-10-01

    Full Text Available The functioning of a modern economic system is based on the interaction of objects of different hierarchical levels. Thus, the problem of the study of innovation processes taking into account the mutual influence of the activities of these economic actors becomes important. The paper dwells evolutionary basis for the formation of models of innovation development on the basis of micro and macroeconomic analysis. Most of the concepts recognized that despite a big number of diverse models, the coordination of the relations between economic agents is of crucial importance for the successful innovation development. According to the results of the evolutionary-hierarchical analysis, the authors reveal key phases of the development of forms of business cooperation, science and government in the domestic economy. It has become the starting point of the conception of the characteristics of the interaction in the cluster models of innovation development of the economy. Considerable expectancies on improvement of the national innovative system are connected with the development of cluster and network structures. The main objective of government authorities is the formation of mechanisms and institutions that will foster cooperation between members of the clusters. The article explains that the clusters cannot become the factors in the growth of the national economy, not being an effective tool for interaction between the actors of the regional innovative systems.

  4. Stuttering, induced fluency, and natural fluency: a hierarchical series of activation likelihood estimation meta-analyses.

    Science.gov (United States)

    Budde, Kristin S; Barron, Daniel S; Fox, Peter T

    2014-12-01

    Developmental stuttering is a speech disorder most likely due to a heritable form of developmental dysmyelination impairing the function of the speech-motor system. Speech-induced brain-activation patterns in persons who stutter (PWS) are anomalous in various ways; the consistency of these aberrant patterns is a matter of ongoing debate. Here, we present a hierarchical series of coordinate-based meta-analyses addressing this issue. Two tiers of meta-analyses were performed on a 17-paper dataset (202 PWS; 167 fluent controls). Four large-scale (top-tier) meta-analyses were performed, two for each subject group (PWS and controls). These analyses robustly confirmed the regional effects previously postulated as "neural signatures of stuttering" (Brown, Ingham, Ingham, Laird, & Fox, 2005) and extended this designation to additional regions. Two smaller-scale (lower-tier) meta-analyses refined the interpretation of the large-scale analyses: (1) a between-group contrast targeting differences between PWS and controls (stuttering trait); and (2) a within-group contrast (PWS only) of stuttering with induced fluency (stuttering state).

  5. Hierarchical clustering

    Directory of Open Access Journals (Sweden)

    L. Infante

    2002-01-01

    Full Text Available En esta contribuci on presento resultados recientes sobre las propiedades de acumulaci on de galaxias, grupos, c umulos y superc umulos de bajo redshift (z 1. Presento, a su vez, lo esperado y lo medido con respecto al grado de evoluci on de la acumulaci on de galaxias. Hemos usado el cat alogo fotom etrico de galaxias extra do de las primeras im agenes del \\Sloan Digital Sky Survey", para estudiar las propiedades de acumulaci on de peque~nas estructuras de galaxias, pares, tr os, cuartetos, quintetos, etc. Un an alisis de la funci on de correlaci on de dos puntos, en un area de 250 grados cuadrados del cielo, muestra que estos objetos, al parecer, est an mucho m as acumulados que galaxias individuales.

  6. Hierarchical Clustering of Large Databases and Classification of Antibiotics at High Noise Levels

    Directory of Open Access Journals (Sweden)

    Alexander V. Yarkov

    2008-12-01

    Full Text Available A new algorithm for divisive hierarchical clustering of chemical compounds based on 2D structural fragments is suggested. The algorithm is deterministic, and given a random ordering of the input, will always give the same clustering and can process a database up to 2 million records on a standard PC. The algorithm was used for classification of 1,183 antibiotics mixed with 999,994 random chemical structures. Similarity threshold, at which best separation of active and non active compounds took place, was estimated as 0.6. 85.7% of the antibiotics were successfully classified at this threshold with 0.4% of inaccurate compounds. A .sdf file was created with the probe molecules for clustering of external databases.

  7. A novel approach to the problem of non-uniqueness of the solution in hierarchical clustering.

    Science.gov (United States)

    Cattinelli, Isabella; Valentini, Giorgio; Paulesu, Eraldo; Borghese, Nunzio Alberto

    2013-07-01

    The existence of multiple solutions in clustering, and in hierarchical clustering in particular, is often ignored in practical applications. However, this is a non-trivial problem, as different data orderings can result in different cluster sets that, in turns, may lead to different interpretations of the same data. The method presented here offers a solution to this issue. It is based on the definition of an equivalence relation over dendrograms that allows developing all and only the significantly different dendrograms for the same dataset, thus reducing the computational complexity to polynomial from the exponential obtained when all possible dendrograms are considered. Experimental results in the neuroimaging and bioinformatics domains show the effectiveness of the proposed method.

  8. A supplier selection using a hybrid grey based hierarchical clustering and artificial bee colony

    Directory of Open Access Journals (Sweden)

    Farshad Faezy Razi

    2014-06-01

    Full Text Available Selection of one or a combination of the most suitable potential providers and outsourcing problem is the most important strategies in logistics and supply chain management. In this paper, selection of an optimal combination of suppliers in inventory and supply chain management are studied and analyzed via multiple attribute decision making approach, data mining and evolutionary optimization algorithms. For supplier selection in supply chain, hierarchical clustering according to the studied indexes first clusters suppliers. Then, according to its cluster, each supplier is evaluated through Grey Relational Analysis. Then the combination of suppliers’ Pareto optimal rank and costs are obtained using Artificial Bee Colony meta-heuristic algorithm. A case study is conducted for a better description of a new algorithm to select a multiple source of suppliers.

  9. Using Dynamic Quantum Clustering to Analyze Hierarchically Heterogeneous Samples on the Nanoscale

    Energy Technology Data Exchange (ETDEWEB)

    Hume, Allison; /Princeton U. /SLAC

    2012-09-07

    Dynamic Quantum Clustering (DQC) is an unsupervised, high visual data mining technique. DQC was tested as an analysis method for X-ray Absorption Near Edge Structure (XANES) data from the Transmission X-ray Microscopy (TXM) group. The TXM group images hierarchically heterogeneous materials with nanoscale resolution and large field of view. XANES data consists of energy spectra for each pixel of an image. It was determined that DQC successfully identifies structure in data of this type without prior knowledge of the components in the sample. Clusters and sub-clusters clearly reflected features of the spectra that identified chemical component, chemical environment, and density in the image. DQC can also be used in conjunction with the established data analysis technique, which does require knowledge of components present.

  10. A hierarchical cluster analysis of normal-tension glaucoma using spectral-domain optical coherence tomography parameters.

    Science.gov (United States)

    Bae, Hyoung Won; Ji, Yongwoo; Lee, Hye Sun; Lee, Naeun; Hong, Samin; Seong, Gong Je; Sung, Kyung Rim; Kim, Chan Yun

    2015-01-01

    Normal-tension glaucoma (NTG) is a heterogenous disease, and there is still controversy about subclassifications of this disorder. On the basis of spectral-domain optical coherence tomography (SD-OCT), we subdivided NTG with hierarchical cluster analysis using optic nerve head (ONH) parameters and retinal nerve fiber layer (RNFL) thicknesses. A total of 200 eyes of 200 NTG patients between March 2011 and June 2012 underwent SD-OCT scans to measure ONH parameters and RNFL thicknesses. We classified NTG into homogenous subgroups based on these variables using a hierarchical cluster analysis, and compared clusters to evaluate diverse NTG characteristics. Three clusters were found after hierarchical cluster analysis. Cluster 1 (62 eyes) had the thickest RNFL and widest rim area, and showed early glaucoma features. Cluster 2 (60 eyes) was characterized by the largest cup/disc ratio and cup volume, and showed advanced glaucomatous damage. Cluster 3 (78 eyes) had small disc areas in SD-OCT and were comprised of patients with significantly younger age, longer axial length, and greater myopia than the other 2 groups. A hierarchical cluster analysis of SD-OCT scans divided NTG patients into 3 groups based upon ONH parameters and RNFL thicknesses. It is anticipated that the small disc area group comprised of younger and more myopic patients may show unique features unlike the other 2 groups.

  11. Water quality assessment with hierarchical cluster analysis based on Mahalanobis distance.

    Science.gov (United States)

    Du, Xiangjun; Shao, Fengjing; Wu, Shunyao; Zhang, Hanlin; Xu, Si

    2017-07-01

    Water quality assessment is crucial for assessment of marine eutrophication, prediction of harmful algal blooms, and environment protection. Previous studies have developed many numeric modeling methods and data driven approaches for water quality assessment. The cluster analysis, an approach widely used for grouping data, has also been employed. However, there are complex correlations between water quality variables, which play important roles in water quality assessment but have always been overlooked. In this paper, we analyze correlations between water quality variables and propose an alternative method for water quality assessment with hierarchical cluster analysis based on Mahalanobis distance. Further, we cluster water quality data collected form coastal water of Bohai Sea and North Yellow Sea of China, and apply clustering results to evaluate its water quality. To evaluate the validity, we also cluster the water quality data with cluster analysis based on Euclidean distance, which are widely adopted by previous studies. The results show that our method is more suitable for water quality assessment with many correlated water quality variables. To our knowledge, it is the first attempt to apply Mahalanobis distance for coastal water quality assessment.

  12. Intensity-based hierarchical clustering in CT-scans: application to interactive segmentation in cardiology

    Science.gov (United States)

    Hadida, Jonathan; Desrosiers, Christian; Duong, Luc

    2011-03-01

    The segmentation of anatomical structures in Computed Tomography Angiography (CTA) is a pre-operative task useful in image guided surgery. Even though very robust and precise methods have been developed to help achieving a reliable segmentation (level sets, active contours, etc), it remains very time consuming both in terms of manual interactions and in terms of computation time. The goal of this study is to present a fast method to find coarse anatomical structures in CTA with few parameters, based on hierarchical clustering. The algorithm is organized as follows: first, a fast non-parametric histogram clustering method is proposed to compute a piecewise constant mask. A second step then indexes all the space-connected regions in the piecewise constant mask. Finally, a hierarchical clustering is achieved to build a graph representing the connections between the various regions in the piecewise constant mask. This step builds up a structural knowledge about the image. Several interactive features for segmentation are presented, for instance association or disassociation of anatomical structures. A comparison with the Mean-Shift algorithm is presented.

  13. Hierarchical Regional Disparities and Potential Sector Identification Using Modified Agglomerative Clustering

    Science.gov (United States)

    Munandar, T. A.; Azhari; Mushdholifah, A.; Arsyad, L.

    2017-03-01

    Disparities in regional development methods are commonly identified using the Klassen Typology and Location Quotient. Both methods typically use the data on the gross regional domestic product (GRDP) sectors of a particular region. The Klassen approach can identify regional disparities by classifying the GRDP sector data into four classes, namely Quadrants I, II, III, and IV. Each quadrant indicates a certain level of regional disparities based on the GRDP sector value of the said region. Meanwhile, the Location Quotient (LQ) is usually used to identify potential sectors in a particular region so as to determine which sectors are potential and which ones are not potential. LQ classifies each sector into three classes namely, the basic sector, the non-basic sector with a competitive advantage, and the non-basic sector which can only meet its own necessities. Both Klassen Typology and LQ are unable to visualize the relationship of achievements in the development clearly of each region and sector. This research aimed to develop a new approach to the identification of disparities in regional development in the form of hierarchical clustering. The method of Hierarchical Agglomerative Clustering (HAC) was employed as the basis of the hierarchical clustering model for identifying disparities in regional development. Modifications were made to HAC using the Klassen Typology and LQ. Then, HAC which had been modified using the Klassen Typology was called MHACK while HAC which had been modified using LQ was called MACLoQ. Both algorithms can be used to identify regional disparities (MHACK) and potential sectors (MACLoQ), respectively, in the form of hierarchical clusters. Based on the MHACK in 31 regencies in Central Java Province, it is identified that 3 regencies (Demak, Jepara, and Magelang City) fall into the category of developed and rapidly-growing regions, while the other 28 regencies fall into the category of developed but depressed regions. Results of the MACLo

  14. Diversity of Xiphinema americanum-group Species and Hierarchical Cluster Analysis of Morphometrics.

    Science.gov (United States)

    Lamberti, F; Ciancio, A

    1993-09-01

    Of the 39 species composing the Xiphinema americanum group, 14 were described originally from North America and two others have been reported from this region. Many species are very similar morphologically and can be distinguished only by a difficult comparison of various combinations of some morphometric characters. Study of morphometrics of 49 populations, including the type populations of the 39 species attributed to this group, by principal component analysis and hierarchical cluster analysis placed the populations into five subgroups, proposed here as the X. brevicolle subgroup (seven species), the X. americanum subgroup (17 species), the X. taylori subgroup (two species), the X. pachtaicum subgroup (eight species), and the X. lambertii subgroup (five species).

  15. Iterative Maps with Hierarchical Clustering for the Observed Scales of Astrophysical and Cosmological Structures

    CERN Document Server

    Capozziello, S; De Siena, S; Guerra, F; Illuminati, F

    2000-01-01

    We derive, in order of magnitude, the observed astrophysical and cosmologicalscales in the Universe, from neutron stars to superclusters of galaxies, up to,asymptotically, the observed radius of the Universe. This result is obtained byintroducing a recursive scheme of alternating hierachical mechanisms ofthree-dimensional and two-dimensional close packings of gravitationallyinteracting objects. The iterative scheme yields a rapidly converging geometricsequence, which can be described as a hierarchical clustering of aggregates,having the observed radius of the Universe as its fixed point.

  16. CLUSTAG & WCLUSTAG: Hierarchical Clustering Algorithms for Efficient Tag-SNP Selection

    Science.gov (United States)

    Ao, Sio-Iong

    More than 6 million single nucleotide polymorphisms (SNPs) in the human genome have been genotyped by the HapMap project. Although only a pro portion of these SNPs are functional, all can be considered as candidate markers for indirect association studies to detect disease-related genetic variants. The complete screening of a gene or a chromosomal region is nevertheless an expensive undertak ing for association studies. A key strategy for improving the efficiency of association studies is to select a subset of informative SNPs, called tag SNPs, for analysis. In the chapter, hierarchical clustering algorithms have been proposed for efficient tag SNP selection.

  17. Hierarchical Agglomerative Clustering Schemes for Energy-Efficiency in Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Taleb Tariq

    2017-06-01

    Full Text Available Extending the lifetime of wireless sensor networks (WSNs while delivering the expected level of service remains a hot research topic. Clustering has been identified in the literature as one of the primary means to save communication energy. In this paper, we argue that hierarchical agglomerative clustering (HAC provides a suitable foundation for designing highly energy efficient communication protocols for WSNs. To this end, we study a new mechanism for selecting cluster heads (CHs based both on the physical location of the sensors and their residual energy. Furthermore, we study different patterns of communications between the CHs and the base station depending on the possible transmission ranges and the ability of the sensors to act as traffic relays. Simulation results show that our proposed clustering and communication schemes outperform well-knows existing approaches by comfortable margins. In particular, networks lifetime is increased by more than 60% compared to LEACH and HEED, and by more than 30% compared to K-means clustering.

  18. Hierarchical Clustering Algorithm based on Attribute Dependency for Attention Deficit Hyperactive Disorder

    Directory of Open Access Journals (Sweden)

    J Anuradha

    2014-05-01

    Full Text Available Attention Deficit Hyperactive Disorder (ADHD is a disruptive neurobehavioral disorder characterized by abnormal behavioral patterns in attention, perusing activity, acting impulsively and combined types. It is predominant among school going children and it is tricky to differentiate between an active and an ADHD child. Misdiagnosis and undiagnosed cases are very common. Behavior patterns are identified by the mentors in the academic environment who lack skills in screening those kids. Hence an unsupervised learning algorithm can cluster the behavioral patterns of children at school for diagnosis of ADHD. In this paper, we propose a hierarchical clustering algorithm to partition the dataset based on attribute dependency (HCAD. HCAD forms clusters of data based on the high dependent attributes and their equivalence relation. It is capable of handling large volumes of data with reasonably faster clustering than most of the existing algorithms. It can work on both labeled and unlabelled data sets. Experimental results reveal that this algorithm has higher accuracy in comparison to other algorithms. HCAD achieves 97% of cluster purity in diagnosing ADHD. Empirical analysis of application of HCAD on different data sets from UCI repository is provided.

  19. Asteroid family identification using the Hierarchical Clustering Method and WISE/NEOWISE physical properties

    CERN Document Server

    Masiero, Joseph R; Bauer, J M; Grav, T; Nugent, C R; Stevenson, R

    2013-01-01

    Using albedos from WISE/NEOWISE to separate distinct albedo groups within the Main Belt asteroids, we apply the Hierarchical Clustering Method to these subpopulations and identify dynamically associated clusters of asteroids. While this survey is limited to the ~35% of known Main Belt asteroids that were detected by NEOWISE, we present the families linked from these objects as higher confidence associations than can be obtained from dynamical linking alone. We find that over one-third of the observed population of the Main Belt is represented in the high-confidence cores of dynamical families. The albedo distribution of family members differs significantly from the albedo distribution of background objects in the same region of the Main Belt, however interpretation of this effect is complicated by the incomplete identification of lower-confidence family members. In total we link 38,298 asteroids into 76 distinct families. This work represents a critical step necessary to debias the albedo and size distributio...

  20. Microglia Morphological Categorization in a Rat Model of Neuroinflammation by Hierarchical Cluster and Principal Components Analysis

    Science.gov (United States)

    Fernández-Arjona, María del Mar; Grondona, Jesús M.; Granados-Durán, Pablo; Fernández-Llebrez, Pedro; López-Ávalos, María D.

    2017-01-01

    It is known that microglia morphology and function are closely related, but only few studies have objectively described different morphological subtypes. To address this issue, morphological parameters of microglial cells were analyzed in a rat model of aseptic neuroinflammation. After the injection of a single dose of the enzyme neuraminidase (NA) within the lateral ventricle (LV) an acute inflammatory process occurs. Sections from NA-injected animals and sham controls were immunolabeled with the microglial marker IBA1, which highlights ramifications and features of the cell shape. Using images obtained by section scanning, individual microglial cells were sampled from various regions (septofimbrial nucleus, hippocampus and hypothalamus) at different times post-injection (2, 4 and 12 h). Each cell yielded a set of 15 morphological parameters by means of image analysis software. Five initial parameters (including fractal measures) were statistically different in cells from NA-injected rats (most of them IL-1β positive, i.e., M1-state) compared to those from control animals (none of them IL-1β positive, i.e., surveillant state). However, additional multimodal parameters were revealed more suitable for hierarchical cluster analysis (HCA). This method pointed out the classification of microglia population in four clusters. Furthermore, a linear discriminant analysis (LDA) suggested three specific parameters to objectively classify any microglia by a decision tree. In addition, a principal components analysis (PCA) revealed two extra valuable variables that allowed to further classifying microglia in a total of eight sub-clusters or types. The spatio-temporal distribution of these different morphotypes in our rat inflammation model allowed to relate specific morphotypes with microglial activation status and brain location. An objective method for microglia classification based on morphological parameters is proposed. Main points Microglia undergo a quantifiable

  1. Comparing methods of analysing datasets with small clusters: case studies using four paediatric datasets.

    Science.gov (United States)

    Marston, Louise; Peacock, Janet L; Yu, Keming; Brocklehurst, Peter; Calvert, Sandra A; Greenough, Anne; Marlow, Neil

    2009-07-01

    Studies of prematurely born infants contain a relatively large percentage of multiple births, so the resulting data have a hierarchical structure with small clusters of size 1, 2 or 3. Ignoring the clustering may lead to incorrect inferences. The aim of this study was to compare statistical methods which can be used to analyse such data: generalised estimating equations, multilevel models, multiple linear regression and logistic regression. Four datasets which differed in total size and in percentage of multiple births (n = 254, multiple 18%; n = 176, multiple 9%; n = 10 098, multiple 3%; n = 1585, multiple 8%) were analysed. With the continuous outcome, two-level models produced similar results in the larger dataset, while generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) produced divergent estimates using the smaller dataset. For the dichotomous outcome, most methods, except generalised least squares multilevel modelling (ML GH 'xtlogit' in Stata) gave similar odds ratios and 95% confidence intervals within datasets. For the continuous outcome, our results suggest using multilevel modelling. We conclude that generalised least squares multilevel modelling (ML GLS 'xtreg' in Stata) and maximum likelihood multilevel modelling (ML MLE 'xtmixed' in Stata) should be used with caution when the dataset is small. Where the outcome is dichotomous and there is a relatively large percentage of non-independent data, it is recommended that these are accounted for in analyses using logistic regression with adjusted standard errors or multilevel modelling. If, however, the dataset has a small percentage of clusters greater than size 1 (e.g. a population dataset of children where there are few multiples) there appears to be less need to adjust for clustering.

  2. Analysis of genetic association in Listeria and Diabetes using Hierarchical Clustering and Silhouette Index

    Science.gov (United States)

    Pagnuco, Inti A.; Pastore, Juan I.; Abras, Guillermo; Brun, Marcel; Ballarin, Virginia L.

    2016-04-01

    It is usually assumed that co-expressed genes suggest co-regulation in the underlying regulatory network. Determining sets of co-expressed genes is an important task, where significative groups of genes are defined based on some criteria. This task is usually performed by clustering algorithms, where the whole family of genes, or a subset of them, are clustered into meaningful groups based on their expression values in a set of experiment. In this work we used a methodology based on the Silhouette index as a measure of cluster quality for individual gene groups, and a combination of several variants of hierarchical clustering to generate the candidate groups, to obtain sets of co-expressed genes for two real data examples. We analyzed the quality of the best ranked groups, obtained by the algorithm, using an online bioinformatics tool that provides network information for the selected genes. Moreover, to verify the performance of the algorithm, considering the fact that it doesn’t find all possible subsets, we compared its results against a full search, to determine the amount of good co-regulated sets not detected.

  3. On the Formation of Cool, Non-Flowing Cores in Galaxy Clusters via Hierarchical Mergers

    CERN Document Server

    Burns, J O; Norman, M L; Bryan, G L

    2003-01-01

    We present a new model for the creation of cool cores in rich galaxy clusters within a LambdaCDM cosmological framework using the results from high spatial dynamic range, adaptive mesh hydro/N-body simulations. It is proposed that cores of cool gas first form in subclusters and these subclusters merge to create rich clusters with cool, central X-Ray excesses. The rich cool clusters do not possess ``cooling flows'' due to the presence of bulk velocities in the intracluster medium in excess of 1000 km/sec produced by on-going accretion of gas from supercluster filaments. This new model has several attractive features including the presence of substantial core substructure within the cool cores, and it predicts the appearance of cool bullets, cool fronts, and cool filaments all of which have been recently observed with X-Ray satellites. This hierarchical formation model is also consistent with the observation that cool cores in Abell clusters occur preferentially in dense supercluster environments. On the other ...

  4. Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data

    Directory of Open Access Journals (Sweden)

    Reilly John J

    2005-06-01

    Full Text Available Abstract Background Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture. Methods A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of a classifier outweigh the loss of resolution incurred through merging. Results Analysis of the COPD data set demonstrated that motor tasks related to ambulation can be reliably discriminated from tasks performed in a seated position with the legs in motion or stationary using two features derived from one accelerometer. Classifying motor tasks within the category of activities related to ambulation requires more advanced techniques. While in certain cases all the tasks could be accurately classified, in others merging clusters associated with different motor tasks was necessary. When merging clusters, it was found that the proposed method could lead to more than 12% improvement in classifier accuracy while retaining resolution of 4 tasks. Conclusion Hierarchical

  5. Using hierarchical clustering methods to classify motor activities of COPD patients from wearable sensor data

    Science.gov (United States)

    Sherrill, Delsey M; Moy, Marilyn L; Reilly, John J; Bonato, Paolo

    2005-01-01

    Background Advances in miniature sensor technology have led to the development of wearable systems that allow one to monitor motor activities in the field. A variety of classifiers have been proposed in the past, but little has been done toward developing systematic approaches to assess the feasibility of discriminating the motor tasks of interest and to guide the choice of the classifier architecture. Methods A technique is introduced to address this problem according to a hierarchical framework and its use is demonstrated for the application of detecting motor activities in patients with chronic obstructive pulmonary disease (COPD) undergoing pulmonary rehabilitation. Accelerometers were used to collect data for 10 different classes of activity. Features were extracted to capture essential properties of the data set and reduce the dimensionality of the problem at hand. Cluster measures were utilized to find natural groupings in the data set and then construct a hierarchy of the relationships between clusters to guide the process of merging clusters that are too similar to distinguish reliably. It provides a means to assess whether the benefits of merging for performance of a classifier outweigh the loss of resolution incurred through merging. Results Analysis of the COPD data set demonstrated that motor tasks related to ambulation can be reliably discriminated from tasks performed in a seated position with the legs in motion or stationary using two features derived from one accelerometer. Classifying motor tasks within the category of activities related to ambulation requires more advanced techniques. While in certain cases all the tasks could be accurately classified, in others merging clusters associated with different motor tasks was necessary. When merging clusters, it was found that the proposed method could lead to more than 12% improvement in classifier accuracy while retaining resolution of 4 tasks. Conclusion Hierarchical clustering methods are relevant

  6. Clustering of galaxies in a hierarchical universe - II. Evolution to high redshift

    Science.gov (United States)

    Kauffmann, Guinevere; Colberg, Jörg M.; Diaferio, Antonaldo; White, Simon D. M.

    1999-08-01

    In hierarchical cosmologies the evolution of galaxy clustering depends both on cosmological quantities such as Omega, Lambda and P(k), which determine how collapsed structures - dark matter haloes - form and evolve, and on the physical processes - cooling, star formation, radiative and hydrodynamic feedback - which drive the formation of galaxies within these merging haloes. In this paper we combine dissipationless cosmological N-body simulations and semi-analytic models of galaxy formation in order to study how these two aspects interact. We focus on the differences in clustering predicted for galaxies of differing luminosity, colour, morphology and star formation rate, and on what these differences can teach us about the galaxy formation process. We show that a `dip' in the amplitude of galaxy correlations between z=0 and z=1 can be an important diagnostic. Such a dip occurs in low-density CDM models, because structure forms early, and dark matter haloes of mass ~10^12M_solar, containing galaxies with luminosities ~L_*, are unbiased tracers of the dark matter over this redshift range; their clustering amplitude then evolves similarly to that of the dark matter. At higher redshifts, bright galaxies become strongly biased and the clustering amplitude increases again. In high density models, structure forms late, and bias evolves much more rapidly. As a result, the clustering amplitude of L_* galaxies remains constant from z=0 to z=1. The strength of these effects is sensitive to sample selection. The dip becomes weaker for galaxies with lower star formation rates, redder colours, higher luminosities and earlier morphological types. We explain why this is the case, and how it is related to the variation with redshift of the abundance and environment of the observed galaxies. We also show that the relative peculiar velocities of galaxies are biased low in our models, but that this effect is never very strong. Studies of clustering evolution as a function of galaxy

  7. DATA CLASSIFICATION WITH NEURAL CLASSIFIER USING RADIAL BASIS FUNCTION WITH DATA REDUCTION USING HIERARCHICAL CLUSTERING

    Directory of Open Access Journals (Sweden)

    M. Safish Mary

    2012-04-01

    Full Text Available Classification of large amount of data is a time consuming process but crucial for analysis and decision making. Radial Basis Function networks are widely used for classification and regression analysis. In this paper, we have studied the performance of RBF neural networks to classify the sales of cars based on the demand, using kernel density estimation algorithm which produces classification accuracy comparable to data classification accuracy provided by support vector machines. In this paper, we have proposed a new instance based data selection method where redundant instances are removed with help of a threshold thus improving the time complexity with improved classification accuracy. The instance based selection of the data set will help reduce the number of clusters formed thereby reduces the number of centers considered for building the RBF network. Further the efficiency of the training is improved by applying a hierarchical clustering technique to reduce the number of clusters formed at every step. The paper explains the algorithm used for classification and for conditioning the data. It also explains the complexities involved in classification of sales data for analysis and decision-making.

  8. Biomolecule-Assisted Hydrothermal Synthesis and Self-Assembly of Bi2Te3 Nanostring-Cluster Hierarchical Structure

    DEFF Research Database (Denmark)

    Mi, Jianli; Lock, Nina; Sun, Ting;

    2010-01-01

    A simple biomolecule-assisted hydrothermal approach has been developed for the fabrication of Bi2Te3 thermoelectric nanomaterials. The product has a nanostring-cluster hierarchical structure which is composed of ordered and aligned platelet-like crystals. The platelets are100 nm in diameter...

  9. Clustering of Galaxies in a Hierarchical Universe 2 evolution to High Redshift

    CERN Document Server

    Kauffmann, G; Diaferio, A; White, S D M; Kauffmann, Guinevere; Colberg, Joerg M.; Diaferio, Antonaldo; White, Simon D.M.

    1998-01-01

    In hierarchical cosmologies the evolution of galaxy clustering depends both on cosmological quantities such as Omega and Lambda, which determine how dark matter halos form and evolve, and on the physical processes - cooling, star formation and feedback - which drive the formation of galaxies within these merging halos. In this paper, we combine dissipationless cosmological N-body simulations and semi-analytic models of galaxy formation in order to study how these two aspects interact. We focus on the differences in clustering predicted for galaxies of differing luminosity, colour, morphology and star formation rate and on what these differences can teach us about the galaxy formation process. We show that a "dip" in the amplitude of galaxy correlations between z=0 and z=1 can be an important diagnostic. Such a dip occurs in low-density CDM models because structure forms early and dark matter halos of 10**12 solar masses, containing galaxies with luminosities around L*, are unbiased tracers of the dark matter ...

  10. Quality Assured Optimal Resource Provisioning and Scheduling Technique Based on Improved Hierarchical Agglomerative Clustering Algorithm (IHAC

    Directory of Open Access Journals (Sweden)

    A. Meenakshi

    2016-08-01

    Full Text Available Resource allocation is the task of convenient resources to different uses. In the context of an resources, entire economy, can be assigned by different means, such as markets or central planning. Cloud computing has become a new age technology that has got huge potentials in enterprises and markets. Clouds can make it possible to access applications and associated data from anywhere. The fundamental motive of the resource allocation is to allot the available resource in the most effective manner. In the initial phase, a representative resource usage distribution for a group of nodes with identical resource usage patterns is evaluated as resource bundle which can be easily employed to locate a group of nodes fulfilling a standard criterion. In the document, an innovative clustering-based resource aggregation viz. the Improved Hierarchal Agglomerative Clustering Algorithm (IHAC is elegantly launched to realize the compact illustration of a set of identically behaving nodes for scalability. In the subsequent phase concerned with energetic resource allocation procedure, the hybrid optimization technique is brilliantly brought in. The novel technique is devised for scheduling functions to cloud resources which duly consider both financial and evaluation expenses. The efficiency of the novel Resource allocation system is assessed by means of several parameters such the reliability, reusability and certain other metrics. The optimal path choice is the consequence of the hybrid optimization approach. The new-fangled technique allocates the available resource based on the optimal path.

  11. Demographic Data Assessment using Novel 3DCCOM Spatial Hierarchical Clustering: A Case Study of Sonipat Block, Haryana

    Directory of Open Access Journals (Sweden)

    Mamta Malik

    2011-09-01

    Full Text Available Cluster detection is a tool employed by GIS scientists who specialize in the field of spatial analysis. This study employed a combination of GIS, RS and a novel 3DCCOM spatial data clustering algorithm to assess the rural demographic development strategies of Sonepat block, Haryana, India. This Study is undertaken in the rural and rural-based district in India to demonstrate the integration of village-level spatial and non-spatial data in GIS environment using Hierarchical Clustering. Spatial clusters of living standard parameters, including family members, male and female population, sex ratio, total male and female education ratio etc. The paper also envisages future development and usefulness of this community GIS, Spatial data clustering tool for grass-root level planning. Any data that showsgeographic (spatial variability can be subject to cluster analysis.

  12. Multichannel biomedical time series clustering via hierarchical probabilistic latent semantic analysis.

    Science.gov (United States)

    Wang, Jin; Sun, Xiangping; Nahavandi, Saeid; Kouzani, Abbas; Wu, Yuchuan; She, Mary

    2014-11-01

    Biomedical time series clustering that automatically groups a collection of time series according to their internal similarity is of importance for medical record management and inspection such as bio-signals archiving and retrieval. In this paper, a novel framework that automatically groups a set of unlabelled multichannel biomedical time series according to their internal structural similarity is proposed. Specifically, we treat a multichannel biomedical time series as a document and extract local segments from the time series as words. We extend a topic model, i.e., the Hierarchical probabilistic Latent Semantic Analysis (H-pLSA), which was originally developed for visual motion analysis to cluster a set of unlabelled multichannel time series. The H-pLSA models each channel of the multichannel time series using a local pLSA in the first layer. The topics learned in the local pLSA are then fed to a global pLSA in the second layer to discover the categories of multichannel time series. Experiments on a dataset extracted from multichannel Electrocardiography (ECG) signals demonstrate that the proposed method performs better than previous state-of-the-art approaches and is relatively robust to the variations of parameters including length of local segments and dictionary size. Although the experimental evaluation used the multichannel ECG signals in a biometric scenario, the proposed algorithm is a universal framework for multichannel biomedical time series clustering according to their structural similarity, which has many applications in biomedical time series management. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

  13. Hierarchical clustering of breast cancer methylomes revealed differentially methylated and expressed breast cancer genes.

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    I-Hsuan Lin

    Full Text Available Oncogenic transformation of normal cells often involves epigenetic alterations, including histone modification and DNA methylation. We conducted whole-genome bisulfite sequencing to determine the DNA methylomes of normal breast, fibroadenoma, invasive ductal carcinomas and MCF7. The emergence, disappearance, expansion and contraction of kilobase-sized hypomethylated regions (HMRs and the hypomethylation of the megabase-sized partially methylated domains (PMDs are the major forms of methylation changes observed in breast tumor samples. Hierarchical clustering of HMR revealed tumor-specific hypermethylated clusters and differential methylated enhancers specific to normal or breast cancer cell lines. Joint analysis of gene expression and DNA methylation data of normal breast and breast cancer cells identified differentially methylated and expressed genes associated with breast and/or ovarian cancers in cancer-specific HMR clusters. Furthermore, aberrant patterns of X-chromosome inactivation (XCI was found in breast cancer cell lines as well as breast tumor samples in the TCGA BRCA (breast invasive carcinoma dataset. They were characterized with differentially hypermethylated XIST promoter, reduced expression of XIST, and over-expression of hypomethylated X-linked genes. High expressions of these genes were significantly associated with lower survival rates in breast cancer patients. Comprehensive analysis of the normal and breast tumor methylomes suggests selective targeting of DNA methylation changes during breast cancer progression. The weak causal relationship between DNA methylation and gene expression observed in this study is evident of more complex role of DNA methylation in the regulation of gene expression in human epigenetics that deserves further investigation.

  14. CHIMERA: Top-down model for hierarchical, overlapping and directed cluster structures in directed and weighted complex networks

    Science.gov (United States)

    Franke, R.

    2016-11-01

    In many networks discovered in biology, medicine, neuroscience and other disciplines special properties like a certain degree distribution and hierarchical cluster structure (also called communities) can be observed as general organizing principles. Detecting the cluster structure of an unknown network promises to identify functional subdivisions, hierarchy and interactions on a mesoscale. It is not trivial choosing an appropriate detection algorithm because there are multiple network, cluster and algorithmic properties to be considered. Edges can be weighted and/or directed, clusters overlap or build a hierarchy in several ways. Algorithms differ not only in runtime, memory requirements but also in allowed network and cluster properties. They are based on a specific definition of what a cluster is, too. On the one hand, a comprehensive network creation model is needed to build a large variety of benchmark networks with different reasonable structures to compare algorithms. On the other hand, if a cluster structure is already known, it is desirable to separate effects of this structure from other network properties. This can be done with null model networks that mimic an observed cluster structure to improve statistics on other network features. A third important application is the general study of properties in networks with different cluster structures, possibly evolving over time. Currently there are good benchmark and creation models available. But what is left is a precise sandbox model to build hierarchical, overlapping and directed clusters for undirected or directed, binary or weighted complex random networks on basis of a sophisticated blueprint. This gap shall be closed by the model CHIMERA (Cluster Hierarchy Interconnection Model for Evaluation, Research and Analysis) which will be introduced and described here for the first time.

  15. New Alzheimer amyloid beta responsive genes identified in human neuroblastoma cells by hierarchical clustering.

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    Markus Uhrig

    Full Text Available Alzheimer's disease (AD is characterized by neuronal degeneration and cell loss. Abeta(42, in contrast to Abeta(40, is thought to be the pathogenic form triggering the pathological cascade in AD. In order to unravel overall gene regulation we monitored the transcriptomic responses to increased or decreased Abeta(40 and Abeta(42 levels, generated and derived from its precursor C99 (C-terminal fragment of APP comprising 99 amino acids in human neuroblastoma cells. We identified fourteen differentially expressed transcripts by hierarchical clustering and discussed their involvement in AD. These fourteen transcripts were grouped into two main clusters each showing distinct differential expression patterns depending on Abeta(40 and Abeta(42 levels. Among these transcripts we discovered an unexpected inverse and strong differential expression of neurogenin 2 (NEUROG2 and KIAA0125 in all examined cell clones. C99-overexpression had a similar effect on NEUROG2 and KIAA0125 expression as a decreased Abeta(42/Abeta(40 ratio. Importantly however, an increased Abeta(42/Abeta(40 ratio, which is typical of AD, had an inverse expression pattern of NEUROG2 and KIAA0125: An increased Abeta(42/Abeta(40 ratio up-regulated NEUROG2, but down-regulated KIAA0125, whereas the opposite regulation pattern was observed for a decreased Abeta(42/Abeta(40 ratio. We discuss the possibilities that the so far uncharacterized KIAA0125 might be a counter player of NEUROG2 and that KIAA0125 could be involved in neurogenesis, due to the involvement of NEUROG2 in developmental neural processes.

  16. Using hierarchical clustering of secreted protein families to classify and rank candidate effectors of rust fungi.

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    Diane G O Saunders

    Full Text Available Rust fungi are obligate biotrophic pathogens that cause considerable damage on crop plants. Puccinia graminis f. sp. tritici, the causal agent of wheat stem rust, and Melampsora larici-populina, the poplar leaf rust pathogen, have strong deleterious impacts on wheat and poplar wood production, respectively. Filamentous pathogens such as rust fungi secrete molecules called disease effectors that act as modulators of host cell physiology and can suppress or trigger host immunity. Current knowledge on effectors from other filamentous plant pathogens can be exploited for the characterisation of effectors in the genome of recently sequenced rust fungi. We designed a comprehensive in silico analysis pipeline to identify the putative effector repertoire from the genome of two plant pathogenic rust fungi. The pipeline is based on the observation that known effector proteins from filamentous pathogens have at least one of the following properties: (i contain a secretion signal, (ii are encoded by in planta induced genes, (iii have similarity to haustorial proteins, (iv are small and cysteine rich, (v contain a known effector motif or a nuclear localization signal, (vi are encoded by genes with long intergenic regions, (vii contain internal repeats, and (viii do not contain PFAM domains, except those associated with pathogenicity. We used Markov clustering and hierarchical clustering to classify protein families of rust pathogens and rank them according to their likelihood of being effectors. Using this approach, we identified eight families of candidate effectors that we consider of high value for functional characterization. This study revealed a diverse set of candidate effectors, including families of haustorial expressed secreted proteins and small cysteine-rich proteins. This comprehensive classification of candidate effectors from these devastating rust pathogens is an initial step towards probing plant germplasm for novel resistance components.

  17. Using Hierarchical Clustering of Secreted Protein Families to Classify and Rank Candidate Effectors of Rust Fungi

    Science.gov (United States)

    Saunders, Diane G. O.; Win, Joe; Cano, Liliana M.; Szabo, Les J.; Kamoun, Sophien; Raffaele, Sylvain

    2012-01-01

    Rust fungi are obligate biotrophic pathogens that cause considerable damage on crop plants. Puccinia graminis f. sp. tritici, the causal agent of wheat stem rust, and Melampsora larici-populina, the poplar leaf rust pathogen, have strong deleterious impacts on wheat and poplar wood production, respectively. Filamentous pathogens such as rust fungi secrete molecules called disease effectors that act as modulators of host cell physiology and can suppress or trigger host immunity. Current knowledge on effectors from other filamentous plant pathogens can be exploited for the characterisation of effectors in the genome of recently sequenced rust fungi. We designed a comprehensive in silico analysis pipeline to identify the putative effector repertoire from the genome of two plant pathogenic rust fungi. The pipeline is based on the observation that known effector proteins from filamentous pathogens have at least one of the following properties: (i) contain a secretion signal, (ii) are encoded by in planta induced genes, (iii) have similarity to haustorial proteins, (iv) are small and cysteine rich, (v) contain a known effector motif or a nuclear localization signal, (vi) are encoded by genes with long intergenic regions, (vii) contain internal repeats, and (viii) do not contain PFAM domains, except those associated with pathogenicity. We used Markov clustering and hierarchical clustering to classify protein families of rust pathogens and rank them according to their likelihood of being effectors. Using this approach, we identified eight families of candidate effectors that we consider of high value for functional characterization. This study revealed a diverse set of candidate effectors, including families of haustorial expressed secreted proteins and small cysteine-rich proteins. This comprehensive classification of candidate effectors from these devastating rust pathogens is an initial step towards probing plant germplasm for novel resistance components. PMID:22238666

  18. Symptom Clusters in People Living with HIV Attending Five Palliative Care Facilities in Two Sub-Saharan African Countries: A Hierarchical Cluster Analysis.

    Science.gov (United States)

    Moens, Katrien; Siegert, Richard J; Taylor, Steve; Namisango, Eve; Harding, Richard

    2015-01-01

    Symptom research across conditions has historically focused on single symptoms, and the burden of multiple symptoms and their interactions has been relatively neglected especially in people living with HIV. Symptom cluster studies are required to set priorities in treatment planning, and to lessen the total symptom burden. This study aimed to identify and compare symptom clusters among people living with HIV attending five palliative care facilities in two sub-Saharan African countries. Data from cross-sectional self-report of seven-day symptom prevalence on the 32-item Memorial Symptom Assessment Scale-Short Form were used. A hierarchical cluster analysis was conducted using Ward's method applying squared Euclidean Distance as the similarity measure to determine the clusters. Contingency tables, X2 tests and ANOVA were used to compare the clusters by patient specific characteristics and distress scores. Among the sample (N=217) the mean age was 36.5 (SD 9.0), 73.2% were female, and 49.1% were on antiretroviral therapy (ART). The cluster analysis produced five symptom clusters identified as: 1) dermatological; 2) generalised anxiety and elimination; 3) social and image; 4) persistently present; and 5) a gastrointestinal-related symptom cluster. The patients in the first three symptom clusters reported the highest physical and psychological distress scores. Patient characteristics varied significantly across the five clusters by functional status (worst functional physical status in cluster one, pclusters two and three, p=0.012); global distress (F=26.8, pcluster one, best for cluster four). The greatest burden is associated with cluster one, and should be prioritised in clinical management. Further symptom cluster research in people living with HIV with longitudinally collected symptom data to test cluster stability and identify common symptom trajectories is recommended.

  19. Structural system identification using degree of freedom-based reduction and hierarchical clustering algorithm

    Science.gov (United States)

    Chang, Seongmin; Baek, Sungmin; Kim, Ki-Ook; Cho, Maenghyo

    2015-06-01

    A system identification method has been proposed to validate finite element models of complex structures using measured modal data. Finite element method is used for the system identification as well as the structural analysis. In perturbation methods, the perturbed system is expressed as a combination of the baseline structure and the related perturbations. The changes in dynamic responses are applied to determine the structural modifications so that the equilibrium may be satisfied in the perturbed system. In practical applications, the dynamic measurements are carried out on a limited number of accessible nodes and associated degrees of freedom. The equilibrium equation is, in principle, expressed in terms of the measured (master, primary) and unmeasured (slave, secondary) degrees of freedom. Only the specified degrees of freedom are included in the equation formulation for identification and the unspecified degrees of freedom are eliminated through the iterative improved reduction scheme. A large number of system parameters are included as the unknown variables in the system identification of large-scaled structures. The identification problem with large number of system parameters requires a large amount of computation time and resources. In the present study, a hierarchical clustering algorithm is applied to reduce the number of system parameters effectively. Numerical examples demonstrate that the proposed method greatly improves the accuracy and efficiency in the inverse problem of identification.

  20. Hierarchical black hole triples in young star clusters: impact of Kozai-Lidov resonance on mergers

    CERN Document Server

    Kimpson, Thomas O; Mapelli, Michela; Ziosi, Brunetto M

    2016-01-01

    Mergers of compact object binaries are one of the most powerful sources of gravitational waves (GWs) in the frequency range of second-generation ground-based gravitational wave detectors (Advanced LIGO and Virgo). Dynamical simulations of young dense star clusters (SCs) indicate that ~27 per cent of all double compact object binaries are members of hierarchical triple systems (HTs). In this paper, we consider 570 HTs composed of three compact objects (black holes or neutron stars) that formed dynamically in N-body simulations of young dense SCs. We simulate them for a Hubble time with a new code based on the Mikkola's algorithmic regularization scheme, including the 2.5 post-Newtonian term. We find that ~88 per cent of the simulated systems develop Kozai-Lidov (KL) oscillations. KL resonance triggers the merger of the inner binary in three systems (corresponding to 0.5 per cent of the simulated HTs), by increasing the eccentricity of the inner binary. Accounting for KL oscillations leads to an increase of the...

  1. Hierarchical black hole triples in young star clusters: impact of Kozai-Lidov resonance on mergers

    Science.gov (United States)

    Kimpson, Thomas O.; Spera, Mario; Mapelli, Michela; Ziosi, Brunetto M.

    2016-12-01

    Mergers of compact-object binaries are one of the most powerful sources of gravitational waves (GWs) in the frequency range of second-generation ground-based GW detectors (advanced LIGO and Virgo). Dynamical simulations of young dense star clusters (SCs) indicate that ˜27 per cent of all double compact-object binaries are members of hierarchical triple systems (HTs). In this paper, we consider 570 HTs composed of three compact objects (black holes or neutron stars) that formed dynamically in N-body simulations of young dense SCs. We simulate them for a Hubble time with a new code based on the Mikkola's algorithmic regularization scheme, including the 2.5 post-Newtonian term. We find that ˜88 per cent of the simulated systems develop Kozai-Lidov (KL) oscillations. KL resonance triggers the merger of the inner binary in three systems (corresponding to 0.5 per cent of the simulated HTs), by increasing the eccentricity of the inner binary. Accounting for KL oscillations leads to an increase of the total expected merger rate by ≈50 per cent. All binaries that merge because of KL oscillations were formed by dynamical exchanges (i.e. none is a primordial binary) and have chirp mass >20 M⊙. This result might be crucial to interpret the formation channel of the first recently detected GW events.

  2. Ingredients and Process Standardization of Thepla: An Indian Unleavened Vegetable Flatbread using Hierarchical Cluster Analysis

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    S.S. Arya

    2012-10-01

    Full Text Available Thepla is an Indian unleavened flatbread made from whole-wheat flour with added spices and vegetables. It is particularly consumed in western zone of the India. The preparation of thepla is tedious, time consuming and requires skill. In the present study standardization of thepla ingredients were carried out by standardizing each ingredient on the basis of Overall Acceptability (OA score. Sensory analysis was carried out using nine-point hedonic rating scale with ten trained panellists. Standardized ingredients of thepla were: salt 3%, red chili powder 2.5%, fenugreek leaves 12%, cumin seed powder 0.6%, coriander seed powder 0.6%, ginger garlic paste (1:1 6%, asafoetida 0.6% and oil 3% w/w of whole wheat flour on the basis of highest sensory OA score. Further thepla process parameters such as time, temperature, diameter of thepla and weight of dough were standardized on the basis of sensory OA score. Obtained sensory score data was processed for Hierarchical Cluster Analysis (HCA.

  3. A new Hierarchical Group Key Management based on Clustering Scheme for Mobile Ad Hoc Networks

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    Ayman EL-SAYED

    2014-05-01

    Full Text Available The migration from wired network to wireless network has been a global trend in the past few decades because they provide anytime-anywhere networking services. The wireless networks are rapidly deployed in the future, secure wireless environment will be mandatory. As well, The mobility and scalability brought by wireless network made it possible in many applications. Among all the contemporary wireless networks,Mobile Ad hoc Networks (MANET is one of the most important and unique applications. MANET is a collection of autonomous nodes or terminals which communicate with each other by forming a multihop radio network and maintaining connectivity in a decentralized manner. Due to the nature of unreliable wireless medium data transfer is a major problem in MANET and it lacks security and reliability of data. The most suitable solution to provide the expected level of security to these services is the provision of a key management protocol. A Key management is vital part of security. This issue is even bigger in wireless network compared to wired network. The distribution of keys in an authenticated manner is a difficult task in MANET. When a member leaves or joins the group, it needs to generate a new key to maintain forward and backward secrecy. In this paper, we propose a new group key management schemes namely a Hierarchical, Simple, Efficient and Scalable Group Key (HSESGK based on clustering management scheme for MANETs and different other schemes are classified. Group members deduce the group key in a distributed manner.

  4. MAP-Based Underdetermined Blind Source Separation of Convolutive Mixtures by Hierarchical Clustering and -Norm Minimization

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    Kellermann Walter

    2007-01-01

    Full Text Available We address the problem of underdetermined BSS. While most previous approaches are designed for instantaneous mixtures, we propose a time-frequency-domain algorithm for convolutive mixtures. We adopt a two-step method based on a general maximum a posteriori (MAP approach. In the first step, we estimate the mixing matrix based on hierarchical clustering, assuming that the source signals are sufficiently sparse. The algorithm works directly on the complex-valued data in the time-frequency domain and shows better convergence than algorithms based on self-organizing maps. The assumption of Laplacian priors for the source signals in the second step leads to an algorithm for estimating the source signals. It involves the -norm minimization of complex numbers because of the use of the time-frequency-domain approach. We compare a combinatorial approach initially designed for real numbers with a second-order cone programming (SOCP approach designed for complex numbers. We found that although the former approach is not theoretically justified for complex numbers, its results are comparable to, or even better than, the SOCP solution. The advantage is a lower computational cost for problems with low input/output dimensions.

  5. Hierarchical Clustering Multi-Task Learning for Joint Human Action Grouping and Recognition.

    Science.gov (United States)

    Liu, An-An; Su, Yu-Ting; Nie, Wei-Zhi; Kankanhalli, Mohan

    2017-01-01

    This paper proposes a hierarchical clustering multi-task learning (HC-MTL) method for joint human action grouping and recognition. Specifically, we formulate the objective function into the group-wise least square loss regularized by low rank and sparsity with respect to two latent variables, model parameters and grouping information, for joint optimization. To handle this non-convex optimization, we decompose it into two sub-tasks, multi-task learning and task relatedness discovery. First, we convert this non-convex objective function into the convex formulation by fixing the latent grouping information. This new objective function focuses on multi-task learning by strengthening the shared-action relationship and action-specific feature learning. Second, we leverage the learned model parameters for the task relatedness measure and clustering. In this way, HC-MTL can attain both optimal action models and group discovery by alternating iteratively. The proposed method is validated on three kinds of challenging datasets, including six realistic action datasets (Hollywood2, YouTube, UCF Sports, UCF50, HMDB51 & UCF101), two constrained datasets (KTH & TJU), and two multi-view datasets (MV-TJU & IXMAS). The extensive experimental results show that: 1) HC-MTL can produce competing performances to the state of the arts for action recognition and grouping; 2) HC-MTL can overcome the difficulty in heuristic action grouping simply based on human knowledge; 3) HC-MTL can avoid the possible inconsistency between the subjective action grouping depending on human knowledge and objective action grouping based on the feature subspace distributions of multiple actions. Comparison with the popular clustered multi-task learning further reveals that the discovered latent relatedness by HC-MTL aids inducing the group-wise multi-task learning and boosts the performance. To the best of our knowledge, ours is the first work that breaks the assumption that all actions are either

  6. Hierarchical clustering of ryanodine receptors enables emergence of a calcium clock in sinoatrial node cells.

    Science.gov (United States)

    Stern, Michael D; Maltseva, Larissa A; Juhaszova, Magdalena; Sollott, Steven J; Lakatta, Edward G; Maltsev, Victor A

    2014-05-01

    rate in response to β-adrenergic stimulation. The model indicates that the hierarchical clustering of surface RyRs in SANCs may be a crucial adaptive mechanism. Pathological desynchronization of the clocks may explain sinus node dysfunction in heart failure and RyR mutations.

  7. Dynamical Analyses of Galaxy Clusters With Large Redshift Samples

    Science.gov (United States)

    Mohr, J. J.; Richstone, D. O.; Wegner, G.

    1998-12-01

    We construct equilibrium models of galaxy orbits in five nearby galaxy clusters to study the distribution of binding mass, the nature of galaxy orbits and the kinematic differences between cluster populations of emission-line and non emission-line galaxies. We avail ourselves of 1718 galaxy redshifts (and 1203 cluster member redshifts) in this Jeans analysis; most of these redshifts are new, coming from multifiber spectroscopic runs on the MDM 2.4m with the Decaspec and queue observing on WIYN with Hydra. In addition to the spectroscopic data we have V and R band CCD mosaics (obtained with the MDM 1.3m) of the Abell region in each of these clusters. Our scientific goals include: (i) a quantitative estimate of the range of binding masses M500 consistent with the optical and X-ray data, (ii) an estimate of the typical galaxy oribital anisotropies required to make the galaxy data consistent with the NFW expectation for the cluster potential, (iii) a better understanding of the systematics inherent in the process of rescaling and ``stacking'' galaxy cluster observations, (iv) a reexamination of the recent CNOC results implying that emission-line (blue) galaxies are an equilibrium population with a more extended radial distribution than their non emission-line (red) galaxy counterparts and (v) a measure of the galaxy contribution to the cluster mass of baryons.

  8. Taxonomy of Manufacturing Flexibility at Manufacturing Companies Using Imperialist Competitive Algorithms, Support Vector Machines and Hierarchical Cluster Analysis

    Directory of Open Access Journals (Sweden)

    M. Khoobiyan

    2017-04-01

    Full Text Available Manufacturing flexibility is a multidimensional concept and manufacturing companies act differently in using these dimensions. The purpose of this study is to investigate taxonomy and identify dominant groups of manufacturing flexibility. Dimensions of manufacturing flexibility are extracted by content analysis of literature and expert judgements. Manufacturing flexibility was measured by using a questionnaire developed to survey managers of manufacturing companies. The sample size was set at 379. To identify dominant groups of flexibility based on dimensions of flexibility determined, Hierarchical Cluster Analysis (HCA, Imperialist Competitive Algorithms (ICAs and Support Vector Machines (SVMs were used by cluster validity indices. The best algorithm for clustering was SVMs with three clusters, designated as leading delivery-based flexibility, frugal flexibility and sufficient plan-based flexibility.

  9. Genomic Analyses of Bacterial Porin-Cytochrome Gene Clusters

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    Liang eShi

    2014-11-01

    Full Text Available The porin-cytochrome (Pcc protein complex is responsible for trans-outer membrane electron transfer during extracellular reduction of Fe(III by the dissimilatory metal-reducing bacterium Geobacter sulfurreducens PCA. The identified and characterized Pcc complex of G. sulfurreducens PCA consists of a porin-like outer-membrane protein, a periplasmic 8-heme c-type cytochrome (c-Cyt and an outer-membrane 12-heme c-Cyt, and the genes encoding the Pcc proteins are clustered in the same regions of genome (i.e., the pcc gene clusters of G. sulfurreducens PCA. A survey of additionally microbial genomes has identified the pcc gene clusters in all sequenced Geobacter spp. and other bacteria from six different phyla, including Anaeromyxobacter dehalogenans 2CP-1, A. dehalogenans 2CP-C, Anaeromyxobacter sp. K, Candidatus Kuenenia stuttgartiensis, Denitrovibrio acetiphilus DSM 12809, Desulfurispirillum indicum S5, Desulfurivibrio alkaliphilus AHT2, Desulfurobacterium thermolithotrophum DSM 11699, Desulfuromonas acetoxidans DSM 684, Ignavibacterium album JCM 16511, and Thermovibrio ammonificans HB-1. The numbers of genes in the pcc gene clusters vary, ranging from two to nine. Similar to the metal-reducing (Mtr gene clusters of other Fe(III-reducing bacteria, such as Shewanella spp., additional genes that encode putative c-Cyts with predicted cellular localizations at the cytoplasmic membrane, periplasm and outer membrane often associate with the pcc gene clusters. This suggests that the Pcc-associated c-Cyts may be part of the pathways for extracellular electron transfer reactions. The presence of pcc gene clusters in the microorganisms that do not reduce solid-phase Fe(III and Mn(IV oxides, such as D. alkaliphilus AHT2 and I. album JCM 16511, also suggests that some of the pcc gene clusters may be involved in extracellular electron transfer reactions with the substrates other than Fe(III and Mn(IV oxides.

  10. A Resting-State Brain Functional Network Study in MDD Based on Minimum Spanning Tree Analysis and the Hierarchical Clustering

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    Xiaowei Li

    2017-01-01

    Full Text Available A large number of studies demonstrated that major depressive disorder (MDD is characterized by the alterations in brain functional connections which is also identifiable during the brain’s “resting-state.” But, in the present study, the approach of constructing functional connectivity is often biased by the choice of the threshold. Besides, more attention was paid to the number and length of links in brain networks, and the clustering partitioning of nodes was unclear. Therefore, minimum spanning tree (MST analysis and the hierarchical clustering were first used for the depression disease in this study. Resting-state electroencephalogram (EEG sources were assessed from 15 healthy and 23 major depressive subjects. Then the coherence, MST, and the hierarchical clustering were obtained. In the theta band, coherence analysis showed that the EEG coherence of the MDD patients was significantly higher than that of the healthy controls especially in the left temporal region. The MST results indicated the higher leaf fraction in the depressed group. Compared with the normal group, the major depressive patients lost clustering in frontal regions. Our findings suggested that there was a stronger brain interaction in the MDD group and a left-right functional imbalance in the frontal regions for MDD controls.

  11. Rapid recognition of drug-resistance/sensitivity in leukemic cells by Fourier transform infrared microspectroscopy and unsupervised hierarchical cluster analysis.

    Science.gov (United States)

    Bellisola, Giuseppe; Cinque, Gianfelice; Vezzalini, Marzia; Moratti, Elisabetta; Silvestri, Giovannino; Redaelli, Sara; Gambacorti Passerini, Carlo; Wehbe, Katia; Sorio, Claudio

    2013-07-21

    We tested the ability of Fourier Transform (FT) InfraRed (IR) microspectroscopy (microFTIR) in combination with unsupervised Hierarchical Cluster Analysis (HCA) in identifying drug-resistance/sensitivity in leukemic cells exposed to tyrosine kinase inhibitors (TKIs). Experiments were carried out in a well-established mouse model of human Chronic Myelogenous Leukemia (CML). Mouse-derived pro-B Ba/F3 cells transfected with and stably expressing the human p210(BCR-ABL) drug-sensitive wild-type BCR-ABL or the V299L or T315I p210(BCR-ABL) drug-resistant BCR-ABL mutants were exposed to imatinib-mesylate (IMA) or dasatinib (DAS). MicroFTIR was carried out at the Diamond IR beamline MIRIAM where the mid-IR absorbance spectra of individual Ba/F3 cells were acquired using the high brilliance IR synchrotron radiation (SR) via aperture of 15 × 15 μm(2) in sizes. A conventional IR source (globar) was used to compare average spectra over 15 cells or more. IR signatures of drug actions were identified by supervised analyses in the spectra of TKI-sensitive cells. Unsupervised HCA applied to selected intervals of wavenumber allowed us to classify the IR patterns of viable (drug-resistant) and apoptotic (drug-sensitive) cells with an accuracy of >95%. The results from microFTIR + HCA analysis were cross-validated with those obtained via immunochemical methods, i.e. immunoblotting and flow cytometry (FC) that resulted directly and significantly correlated. We conclude that this combined microFTIR + HCA method potentially represents a rapid, convenient and robust screening approach to study the impact of drugs in leukemic cells as well as in peripheral blasts from patients in clinical trials with new anti-leukemic drugs.

  12. Symptom Clusters in People Living with HIV Attending Five Palliative Care Facilities in Two Sub-Saharan African Countries: A Hierarchical Cluster Analysis.

    Directory of Open Access Journals (Sweden)

    Katrien Moens

    Full Text Available Symptom research across conditions has historically focused on single symptoms, and the burden of multiple symptoms and their interactions has been relatively neglected especially in people living with HIV. Symptom cluster studies are required to set priorities in treatment planning, and to lessen the total symptom burden. This study aimed to identify and compare symptom clusters among people living with HIV attending five palliative care facilities in two sub-Saharan African countries.Data from cross-sectional self-report of seven-day symptom prevalence on the 32-item Memorial Symptom Assessment Scale-Short Form were used. A hierarchical cluster analysis was conducted using Ward's method applying squared Euclidean Distance as the similarity measure to determine the clusters. Contingency tables, X2 tests and ANOVA were used to compare the clusters by patient specific characteristics and distress scores.Among the sample (N=217 the mean age was 36.5 (SD 9.0, 73.2% were female, and 49.1% were on antiretroviral therapy (ART. The cluster analysis produced five symptom clusters identified as: 1 dermatological; 2 generalised anxiety and elimination; 3 social and image; 4 persistently present; and 5 a gastrointestinal-related symptom cluster. The patients in the first three symptom clusters reported the highest physical and psychological distress scores. Patient characteristics varied significantly across the five clusters by functional status (worst functional physical status in cluster one, p<0.001; being on ART (highest proportions for clusters two and three, p=0.012; global distress (F=26.8, p<0.001, physical distress (F=36.3, p<0.001 and psychological distress subscale (F=21.8, p<0.001 (all subscales worst for cluster one, best for cluster four.The greatest burden is associated with cluster one, and should be prioritised in clinical management. Further symptom cluster research in people living with HIV with longitudinally collected symptom data to

  13. Personality change over 40 years of adulthood: hierarchical linear modeling analyses of two longitudinal samples.

    Science.gov (United States)

    Helson, Ravenna; Jones, Constance; Kwan, Virginia S Y

    2002-09-01

    Normative personality change over 40 years was shown in 2 longitudinal cohorts with hierarchical linear modeling of California Psychological Inventory data obtained at multiple times between ages 21-75. Although themes of change and the paucity of differences attributable to gender and cohort largely supported findings of multiethnic cross-sectional samples, the authors also found much quadratic change and much individual variability. The form of quadratic change supported predictions about the influence of period of life and social climate as factors in change over the adult years: Scores on Dominance and Independence peaked in the middle age of both cohorts, and scores on Responsibility were lowest during peak years of the culture of individualism. The idea that personality change is most pronounced before age 30 and then reaches a plateau received no support.

  14. Principal component analysis vs. self-organizing maps combined with hierarchical clustering for pattern recognition in volcano seismic spectra

    Science.gov (United States)

    Unglert, K.; Radić, V.; Jellinek, A. M.

    2016-06-01

    Variations in the spectral content of volcano seismicity related to changes in volcanic activity are commonly identified manually in spectrograms. However, long time series of monitoring data at volcano observatories require tools to facilitate automated and rapid processing. Techniques such as self-organizing maps (SOM) and principal component analysis (PCA) can help to quickly and automatically identify important patterns related to impending eruptions. For the first time, we evaluate the performance of SOM and PCA on synthetic volcano seismic spectra constructed from observations during two well-studied eruptions at Klauea Volcano, Hawai'i, that include features observed in many volcanic settings. In particular, our objective is to test which of the techniques can best retrieve a set of three spectral patterns that we used to compose a synthetic spectrogram. We find that, without a priori knowledge of the given set of patterns, neither SOM nor PCA can directly recover the spectra. We thus test hierarchical clustering, a commonly used method, to investigate whether clustering in the space of the principal components and on the SOM, respectively, can retrieve the known patterns. Our clustering method applied to the SOM fails to detect the correct number and shape of the known input spectra. In contrast, clustering of the data reconstructed by the first three PCA modes reproduces these patterns and their occurrence in time more consistently. This result suggests that PCA in combination with hierarchical clustering is a powerful practical tool for automated identification of characteristic patterns in volcano seismic spectra. Our results indicate that, in contrast to PCA, common clustering algorithms may not be ideal to group patterns on the SOM and that it is crucial to evaluate the performance of these tools on a control dataset prior to their application to real data.

  15. Adjusting the Scott-Knott cluster analyses for unbalanced designs

    Directory of Open Access Journals (Sweden)

    Thiago Vincenzi Conrado

    2016-12-01

    Full Text Available The Scott-Knott cluster analysis is an alternative approach to mean comparisons with high power and no subset overlapping. It is well suited for the statistical challenges in agronomy associated with testing new cultivars, crop treatments, or methods. The original Scott-Knott test was developed to be used under balanced designs; therefore, the loss of a single plot can significantly increase the rate of type I error. In order to avoid type I error inflation from missing plots, we propose an adjustment that maintains power similar to the original test while adding error protection. The proposed adjustment was validated from more than 40 million simulated experiments following the Monte Carlo method. The results indicate a minimal loss of power with a satisfactory type I error control, while keeping the features of the original procedure. A user-friendly SAS macro is provided for this analysis.

  16. The Chinese Family Assessment Instrument (C-FAI): Hierarchical Confirmatory Factor Analyses and Factorial Invariance

    Science.gov (United States)

    Shek, Daniel T. L.; Ma, Cecilia M. S.

    2010-01-01

    Objective: This paper examines the dimensionality and factorial invariance of the Chinese Family Assessment Instrument (C-FAI) using multigroup confirmatory factor analyses (MCFAs). Method: A total of 3,649 students responded to the C-FAI in a community survey. Results: Results showed that there are five dimensions of the C-FAI (communication,…

  17. A new hierarchical Bayesian approach to analyse environmental and climatic influences on debris flow occurrence

    Science.gov (United States)

    Jomelli, Vincent; Pavlova, Irina; Eckert, Nicolas; Grancher, Delphine; Brunstein, Daniel

    2015-12-01

    How can debris flow occurrences be modelled at regional scale and take both environmental and climatic conditions into account? And, of the two, which has the most influence on debris flow activity? In this paper, we try to answer these questions with an innovative Bayesian hierarchical probabilistic model that simultaneously accounts for how debris flows respond to environmental and climatic variables. In it, full decomposition of space and time effects in occurrence probabilities is assumed, revealing an environmental and a climatic trend shared by all years/catchments, respectively, clearly distinguished from residual "random" effects. The resulting regional and annual occurrence probabilities evaluated as functions of the covariates make it possible to weight the respective contribution of the different terms and, more generally, to check the model performances at different spatio-temporal scales. After suitable validation, the model can be used to make predictions at undocumented sites and could be used in further studies for predictions under future climate conditions. Also, the Bayesian paradigm easily copes with missing data, thus making it possible to account for events that may have been missed during surveys. As a case study, we extract 124 debris flow event triggered between 1970 and 2005 in 27 catchments located in the French Alps from the French national natural hazard survey and model their variability of occurrence considering environmental and climatic predictors at the same time. We document the environmental characteristics of each debris flow catchment (morphometry, lithology, land cover, and the presence of permafrost). We also compute 15 climate variables including mean temperature and precipitation between May and October and the number of rainy days with daily cumulative rainfall greater than 10/15/20/25/30/40 mm day- 1. Application of our model shows that the combination of environmental and climatic predictors explained 77% of the overall

  18. Hierarchical linear modeling analyses of the NEO-PI-R scales in the Baltimore Longitudinal Study of Aging.

    Science.gov (United States)

    Terracciano, Antonio; McCrae, Robert R; Brant, Larry J; Costa, Paul T

    2005-09-01

    The authors examined age trends in the 5 factors and 30 facets assessed by the Revised NEO Personality Inventory in Baltimore Longitudinal Study of Aging data (N=1,944; 5,027 assessments) collected between 1989 and 2004. Consistent with cross-sectional results, hierarchical linear modeling analyses showed gradual personality changes in adulthood: a decline in Neuroticism up to age 80, stability and then decline in Extraversion, decline in Openness, increase in Agreeableness, and increase in Conscientiousness up to age 70. Some facets showed different curves from the factor they define. Birth cohort effects were modest, and there were no consistent Gender x Age interactions. Significant nonnormative changes were found for all 5 factors; they were not explained by attrition but might be due to genetic factors, disease, or life experience. Copyright (c) 2005 APA, all rights reserved.

  19. Hierarchical Linear Modeling Analyses of NEO-PI-R Scales In the Baltimore Longitudinal Study of Aging

    Science.gov (United States)

    Terracciano, Antonio; McCrae, Robert R.; Brant, Larry J.; Costa, Paul T.

    2009-01-01

    We examined age trends in the five factors and 30 facets assessed by the Revised NEO Personality Inventory in Baltimore Longitudinal Study of Aging data (N = 1,944; 5,027 assessments) collected between 1989 and 2004. Consistent with cross-sectional results, Hierarchical Linear Modeling analyses showed gradual personality changes in adulthood: a decline up to age 80 in Neuroticism, stability and then decline in Extraversion, decline in Openness, increase in Agreeableness, and increase up to age 70 in Conscientiousness. Some facets showed different curves from the factor they define. Birth cohort effects were modest, and there were no consistent Gender × Age interactions. Significant non-normative changes were found for all five factors; they were not explained by attrition but might be due to genetic factors, disease, or life experience. PMID:16248708

  20. Investigating the effects of climate variations on bacillary dysentery incidence in northeast China using ridge regression and hierarchical cluster analysis

    Directory of Open Access Journals (Sweden)

    Guo Junqiao

    2008-09-01

    Full Text Available Abstract Background The effects of climate variations on bacillary dysentery incidence have gained more recent concern. However, the multi-collinearity among meteorological factors affects the accuracy of correlation with bacillary dysentery incidence. Methods As a remedy, a modified method to combine ridge regression and hierarchical cluster analysis was proposed for investigating the effects of climate variations on bacillary dysentery incidence in northeast China. Results All weather indicators, temperatures, precipitation, evaporation and relative humidity have shown positive correlation with the monthly incidence of bacillary dysentery, while air pressure had a negative correlation with the incidence. Ridge regression and hierarchical cluster analysis showed that during 1987–1996, relative humidity, temperatures and air pressure affected the transmission of the bacillary dysentery. During this period, all meteorological factors were divided into three categories. Relative humidity and precipitation belonged to one class, temperature indexes and evaporation belonged to another class, and air pressure was the third class. Conclusion Meteorological factors have affected the transmission of bacillary dysentery in northeast China. Bacillary dysentery prevention and control would benefit from by giving more consideration to local climate variations.

  1. Selection of key ambient particulate variables for epidemiological studies - applying cluster and heatmap analyses as tools for data reduction.

    Science.gov (United States)

    Gu, Jianwei; Pitz, Mike; Breitner, Susanne; Birmili, Wolfram; von Klot, Stephanie; Schneider, Alexandra; Soentgen, Jens; Reller, Armin; Peters, Annette; Cyrys, Josef

    2012-10-01

    The success of epidemiological studies depends on the use of appropriate exposure variables. The purpose of this study is to extract a relatively small selection of variables characterizing ambient particulate matter from a large measurement data set. The original data set comprised a total of 96 particulate matter variables that have been continuously measured since 2004 at an urban background aerosol monitoring site in the city of Augsburg, Germany. Many of the original variables were derived from measured particle size distribution (PSD) across the particle diameter range 3 nm to 10 μm, including size-segregated particle number concentration, particle length concentration, particle surface concentration and particle mass concentration. The data set was complemented by integral aerosol variables. These variables were measured by independent instruments, including black carbon, sulfate, particle active surface concentration and particle length concentration. It is obvious that such a large number of measured variables cannot be used in health effect analyses simultaneously. The aim of this study is a pre-screening and a selection of the key variables that will be used as input in forthcoming epidemiological studies. In this study, we present two methods of parameter selection and apply them to data from a two-year period from 2007 to 2008. We used the agglomerative hierarchical cluster method to find groups of similar variables. In total, we selected 15 key variables from 9 clusters which are recommended for epidemiological analyses. We also applied a two-dimensional visualization technique called "heatmap" analysis to the Spearman correlation matrix. 12 key variables were selected using this method. Moreover, the positive matrix factorization (PMF) method was applied to the PSD data to characterize the possible particle sources. Correlations between the variables and PMF factors were used to interpret the meaning of the cluster and the heatmap analyses

  2. Energy Efficient Backoff Hierarchical Clustering Algorithms for Multi-Hop Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    Jun Wang; Yong-Tao Cao; Jun-Yuan Xie; Shi-Fu Chen

    2011-01-01

    Compared with flat routing protocols, clustering is a fundamental performance improvement technique in wireless sensor networks, which can increase network scalability and lifetime. In this paper, we integrate the multi-hop technique with a backoff-based clustering algorithm to organize sensors. By using an adaptive backoff strategy, the algorithm not only realizes load balance among sensor node, but also achieves fairly uniform cluster head distribution across the network. Simulation results also demonstrate our algorithm is more energy-efficient than classical ones. Our algorithm is also easily extended to generate a hierarchy of cluster heads to obtain better network management and energy-efficiency.

  3. [The hierarchical clustering analysis of hyperspectral image based on probabilistic latent semantic analysis].

    Science.gov (United States)

    Yi, Wen-Bin; Shen, Li; Qi, Yin-Feng; Tang, Hong

    2011-09-01

    The paper introduces the Probabilistic Latent Semantic Analysis (PLSA) to the image clustering and an effective image clustering algorithm using the semantic information from PLSA is proposed which is used for hyperspectral images. Firstly, the ISODATA algorithm is used to obtain the initial clustering result of hyperspectral image and the clusters of the initial clustering result are considered as the visual words of the PLSA. Secondly, the object-oriented image segmentation algorithm is used to partition the hyperspectral image and segments with relatively pure pixels are regarded as documents in PLSA. Thirdly, a variety of identification methods which can estimate the best number of cluster centers is combined to get the number of latent semantic topics. Then the conditional distributions of visual words in topics and the mixtures of topics in different documents are estimated by using PLSA. Finally, the conditional probabilistic of latent semantic topics are distinguished using statistical pattern recognition method, the topic type for each visual in each document will be given and the clustering result of hyperspectral image are then achieved. Experimental results show the clusters of the proposed algorithm are better than K-MEANS and ISODATA in terms of object-oriented property and the clustering result is closer to the distribution of real spatial distribution of surface.

  4. Clusters, Graphs, and Networks for Analysing Internet-Web-Supported Communication within a Virtual Community

    CERN Document Server

    Polanco, Xavier

    2002-01-01

    The proposal is to use clusters, graphs and networks as models in order to analyse the Web structure. Clusters, graphs and networks provide knowledge representation and organization. Clusters were generated by co-site analysis. The sample is a set of academic Web sites from the countries belonging to the European Union. These clusters are here revisited from the point of view of graph theory and social network analysis. This is a quantitative and structural analysis. In fact, the Internet is a computer network that connects people and organizations. Thus we may consider it to be a social network. The set of Web academic sites represents an empirical social network, and is viewed as a virtual community. The network structural properties are here analysed applying together cluster analysis, graph theory and social network analysis.

  5. Manual hierarchical clustering of regional geochemical data using a Bayesian finite mixture model

    Science.gov (United States)

    Ellefsen, Karl J.; Smith, David

    2016-01-01

    Interpretation of regional scale, multivariate geochemical data is aided by a statistical technique called “clustering.” We investigate a particular clustering procedure by applying it to geochemical data collected in the State of Colorado, United States of America. The clustering procedure partitions the field samples for the entire survey area into two clusters. The field samples in each cluster are partitioned again to create two subclusters, and so on. This manual procedure generates a hierarchy of clusters, and the different levels of the hierarchy show geochemical and geological processes occurring at different spatial scales. Although there are many different clustering methods, we use Bayesian finite mixture modeling with two probability distributions, which yields two clusters. The model parameters are estimated with Hamiltonian Monte Carlo sampling of the posterior probability density function, which usually has multiple modes. Each mode has its own set of model parameters; each set is checked to ensure that it is consistent both with the data and with independent geologic knowledge. The set of model parameters that is most consistent with the independent geologic knowledge is selected for detailed interpretation and partitioning of the field samples.

  6. Energy Efficient Zone Division Multihop Hierarchical Clustering Algorithm for Load Balancing in Wireless Sensor Network

    Directory of Open Access Journals (Sweden)

    Ashim Kumar Ghosh

    2011-12-01

    Full Text Available Wireless sensor nodes are use most embedded computing application. Multihop cluster hierarchy has been presented for large wireless sensor networks (WSNs that can provide scalable routing, data aggregation, and querying. The energy consumption rate for sensors in a WSN varies greatly based on the protocols the sensors use for communications. In this paper we present a cluster based routing algorithm. One of our main goals is to design the energy efficient routing protocol. Here we try to solve the usual problems of WSNs. We know the efficiency of WSNs depend upon the distance between node to base station and the amount of data to be transferred and the performance of clustering is greatly influenced by the selection of cluster-heads, which are in charge of creating clusters and controlling member nodes. This algorithm makes the best use of node with low number of cluster head know as super node. Here we divided the full region in four equal zones and the centre area of the region is used to select for super node. Each zone is considered separately and the zone may be or not divided further that’s depending upon the density of nodes in that zone and capability of the super node. This algorithm forms multilayer communication. The no of layer depends on the network current load and statistics. Our algorithm is easily extended to generate a hierarchy of cluster heads to obtain better network management and energy efficiency.

  7. Classification of cancer cell lines using an automated two-dimensional liquid mapping method with hierarchical clustering techniques.

    Science.gov (United States)

    Wang, Yanfei; Wu, Rong; Cho, Kathleen R; Shedden, Kerby A; Barder, Timothy J; Lubman, David M

    2006-01-01

    A two-dimensional liquid mapping method was used to map the protein expression of eight ovarian serous carcinoma cell lines and three immortalized ovarian surface epithelial cell lines. Maps were produced using pI as the separation parameter in the first dimension and hydrophobicity based upon reversed-phase HPLC separation in the second dimension. The method can be reproducibly used to produce protein expression maps over a pH range from 4.0 to 8.5. A dynamic programming method was used to correct for minor shifts in peaks during the HPLC gradient between sample runs. The resulting corrected maps can then be compared using hierarchical clustering to produce dendrograms indicating the relationship between different cell lines. It was found that several of the ovarian surface epithelial cell lines clustered together, whereas specific groups of serous carcinoma cell lines clustered with each other. Although there is limited information on the current biology of these cell lines, it was shown that the protein expression of certain cell lines is closely related to each other. Other cell lines, including one ovarian clear cell carcinoma cell line, two endometrioid carcinoma cell lines, and three breast epithelial cell lines, were also mapped for comparison to show that their protein profiles cluster differently than the serous samples and to study how they cluster relative to each other. In addition, comparisons can be made between proteins differentially expressed between cell lines that may serve as markers of ovarian serous carcinomas. The automation of the method allows reproducible comparison of many samples, and the use of differential analysis limits the number of proteins that might require further analysis by mass spectrometry techniques.

  8. Cluster based hierarchical resource searching model in P2P network

    Institute of Scientific and Technical Information of China (English)

    Yang Ruijuan; Liu Jian; Tian Jingwen

    2007-01-01

    For the problem of large network load generated by the Gnutella resource-searching model in Peer to Peer (P2P) network, a improved model to decrease the network expense is proposed, which establishes a duster in P2P network,auto-organizes logical layers, and applies a hybrid mechanism of directional searching and flooding. The performance analysis and simulation results show that the proposed hierarchical searching model has availably reduced the generated message load and that its searching-response time performance is as fairly good as that of the Gnutella model.

  9. Inter-Cluster Routing Authentication for Ad Hoc Networks by a Hierarchical Key Scheme

    Institute of Scientific and Technical Information of China (English)

    Yueh-Min Huang; Hua-Yi Lin; Tzone-I Wang

    2006-01-01

    Dissimilar to traditional networks, the features of mobile wireless devices that can actively form a network without any infrastructure mean that mobile ad hoc networks frequently display partition due to node mobility or link failures. These indicate that an ad hoc network is difficult to provide on-line access to a trusted authority server. Therefore,applying traditional Public Key Infrastructure (PKI) security framework to mobile ad hoc networks will cause insecurities.This study proposes a scalable and elastic key management scheme integrated into Cluster Based Secure Routing Protocol (CBSRP) to enhance security and non-repudiation of routing authentication, and introduces an ID-Based internal routing authentication scheme to enhance the routing performance in an internal cluster. Additionally, a method of performing routing authentication between internal and external clusters, as well as inter-cluster routing authentication, is developed.The proposed cluster-based key management scheme distributes trust to an aggregation of cluster heads using a threshold scheme faculty, provides Certificate Authority (CA) with a fault tolerance mechanism to prevent a single point of compromise or failure, and saves CA large repositories from maintaining member certificates, making ad hoc networks robust to malicious behaviors and suitable for numerous mobile devices.

  10. From Snakes to Stars, the Statistics of Collapsed Objects - II. Testing a Generic Scaling Ansatz for Hierarchical Clustering

    CERN Document Server

    Munshi, D; Melott, A L; Munshi, Dipak; Coles, Peter; Melott, Adrian L.

    1999-01-01

    We develop a diagrammatic technique to represent the multi-point cumulative probability density function (CPDF) of mass fluctuations in terms of the statistical properties of individual collapsed objects and relate this to other statistical descriptors such as cumulants, cumulant correlators and factorial moments. We use this approach to establish key scaling relations describing various measurable statistical quantities if clustering follows a simple general scaling ansatz, as expected in hierarchical models. We test these detailed predictions against high-resolution numerical simulations. We show that, when appropriate variables are used, the count probability distribution function (CPDF) and void probability distribution function (VPF) shows clear scaling properties in the non-linear regime. Generalising the results to the two-point count probability distribution function (2CPDF), and the bivariate void probability function (2VPF) we find good match with numerical simulations. We explore the behaviour of t...

  11. Comparative analyses of vertebrate posterior HoxD clusters reveal atypical cluster architecture in the caecilian Typhlonectes natans

    Directory of Open Access Journals (Sweden)

    Amemiya Chris T

    2010-11-01

    Full Text Available Abstract Background The posterior genes of the HoxD cluster play a crucial role in the patterning of the tetrapod limb. This region is under the control of a global, long-range enhancer that is present in all vertebrates. Variation in limb types, as is the case in amphibians, can probably not only be attributed to variation in Hox genes, but is likely to be the product of differences in gene regulation. With a collection of vertebrate genome sequences available today, we used a comparative genomics approach to study the posterior HoxD cluster of amphibians. A frog and a caecilian were included in the study to compare coding sequences as well as to determine the gain and loss of putative regulatory sequences. Results We sequenced the posterior end of the HoxD cluster of a caecilian and performed comparative analyses of this region using HoxD clusters of other vertebrates. We determined the presence of conserved non-coding sequences and traced gains and losses of these footprints during vertebrate evolution, with particular focus on amphibians. We found that the caecilian HoxD cluster is almost three times larger than its mammalian counterpart. This enlargement is accompanied with the loss of one gene and the accumulation of repeats in that area. A similar phenomenon was observed in the coelacanth, where a different gene was lost and expansion of the area where the gene was lost has occurred. At least one phylogenetic footprint present in all vertebrates was lost in amphibians. This conserved region is a known regulatory element and functions as a boundary element in neural tissue to prevent expression of Hoxd genes. Conclusion The posterior part of the HoxD cluster of Typhlonectes natans is among the largest known today. The loss of Hoxd-12 and the expansion of the intergenic region may exert an influence on the limb enhancer, by having to bypass a distance seven times that of regular HoxD clusters. Whether or not there is a correlation with the

  12. 建筑物层次空间聚类方法研究%Hierarchical spatial clustering of buildings

    Institute of Scientific and Technical Information of China (English)

    邓敏; 孙前虎; 文小岳; 徐枫

    2011-01-01

    建筑物空间聚类是实现居民地地图自动综合的有效方法.基于图论和Gestalt原理,发展了一种层次的建筑物聚类方法.该方法可以深层次地挖掘建筑物图形的视觉特性,将面状地物信息充分合理地表达在聚类结果中.依据视觉感知原理,借助Dealaunay三角网构建方法,分析了地图上建筑物的自身形状特性和相互间的邻接关系,并依据建筑物间的可视区域均值距离建立了加权邻近结构图,确定了建筑物的邻近关系(定性约束).根据Gestalt准则将邻近性、方向性和几何特征等量化为旋转卡壳距离约束和几何相似度约束.通过实例验证了层次聚类方法得到更加符合人类认知的建筑物聚类结果.%Spatial clustering provides an effective approach for generalization of residential area in automated cartographic generalization.Based on graph theory and Gestalt principle, a hierarchical approach is proposed in this paper.This approach can be utilized to discover the graphical structure formed by buildings, which is obtained with the consideration of shape, size and neighboring relations.The neighboring relations are determined by Dclaunay triangulation, which is a qualitative constraint among buildings.A weighted neighboring structural graph is obtained by setting visual distance as the weight of the linking edge between adjacent buildings.Two levels of quantitative constraints are developed by considering the Gestalt factors, I.e.proximity, orientation and geometry of buildings.One is the rotating calipers minimum distance;the other is the geometric similarity measure.Through experiments it is illustrated that the results by the hierarchical spatial clustering proposed in this paper are consistent with human perception.

  13. Hierarchical cluster analysis and chemical characterisation of Myrtus communis L. essential oil from Yemen region and its antimicrobial, antioxidant and anti-colorectal adenocarcinoma properties.

    Science.gov (United States)

    Anwar, Sirajudheen; Crouch, Rebecca A; Awadh Ali, Nasser A; Al-Fatimi, Mohamed A; Setzer, William N; Wessjohann, Ludger

    2017-01-09

    The hydrodistilled essential oil obtained from the dried leaves of Myrtus communis, collected in Yemen, was analysed by GC-MS. Forty-one compounds were identified, representing 96.3% of the total oil. The major constituents of essential oil were oxygenated monoterpenoids (87.1%), linalool (29.1%), 1,8-cineole (18.4%), α-terpineol (10.8%), geraniol (7.3%) and linalyl acetate (7.4%). The essential oil was assessed for its antimicrobial activity using a disc diffusion assay and resulted in moderate to potent antibacterial and antifungal activities targeting mainly Bacillus subtilis, Staphylococcus aureus and Candida albicans. The oil moderately reduced the diphenylpicrylhydrazyl radical (IC50 = 4.2 μL/mL or 4.1 mg/mL). In vitro cytotoxicity evaluation against HT29 (human colonic adenocarcinoma cells) showed that the essential oil exhibited a moderate antitumor effect with IC50 of 110 ± 4 μg/mL. Hierarchical cluster analysis of M. communis has been carried out based on the chemical compositions of 99 samples reported in the literature, including Yemeni sample.

  14. Analytical relations concerning the collapse time in hierarchically clustered cosmological models

    CERN Document Server

    Gambera, M

    1997-01-01

    By means of numerical methods, we solve the equations of motion for the collapse of a shell of baryonic matter, made of galaxies and substructure falling into the central regions of a cluster of galaxies, taking into account the effect of the dynamical friction. The parameters on which the dynamical friction mainly depends are: the peaks' height, the number of peaks inside a protocluster multiplied by the correlation function evaluated at the origin, the filtering radius and the nucleus radius of the protocluster of galaxies. We show how the collapse time (Tau) of the shell depends on these parameters. We give a formula that links the dynamical friction coefficient (Eta) o the parameters mentioned above and an analytic relation between the collapse time and (Eta). Finally, we obtain an analytical relation between (Tau) and the mean overdensity (mean Delta) within the shell. All the analytical relations that we find are in excellent agreement with the numerical integration.

  15. Hierarchical cluster analysis of labour market regulations and population health: a taxonomy of low- and middle-income countries

    Directory of Open Access Journals (Sweden)

    Muntaner Carles

    2012-04-01

    Full Text Available Abstract Background An important contribution of the social determinants of health perspective has been to inquire about non-medical determinants of population health. Among these, labour market regulations are of vital significance. In this study, we investigate the labour market regulations among low- and middle-income countries (LMICs and propose a labour market taxonomy to further understand population health in a global context. Methods Using Gross National Product per capita, we classify 113 countries into either low-income (n = 71 or middle-income (n = 42 strata. Principal component analysis of three standardized indicators of labour market inequality and poverty is used to construct 2 factor scores. Factor score reliability is evaluated with Cronbach's alpha. Using these scores, we conduct a hierarchical cluster analysis to produce a labour market taxonomy, conduct zero-order correlations, and create box plots to test their associations with adult mortality, healthy life expectancy, infant mortality, maternal mortality, neonatal mortality, under-5 mortality, and years of life lost to communicable and non-communicable diseases. Labour market and health data are retrieved from the International Labour Organization's Key Indicators of Labour Markets and World Health Organization's Statistical Information System. Results Six labour market clusters emerged: Residual (n = 16, Emerging (n = 16, Informal (n = 10, Post-Communist (n = 18, Less Successful Informal (n = 22, and Insecure (n = 31. Primary findings indicate: (i labour market poverty and population health is correlated in both LMICs; (ii association between labour market inequality and health indicators is significant only in low-income countries; (iii Emerging (e.g., East Asian and Eastern European countries and Insecure (e.g., sub-Saharan African nations clusters are the most advantaged and disadvantaged, respectively, with the remaining clusters experiencing levels of population

  16. Typing of unknown microorganisms based on quantitative analysis of fatty acids by mass spectrometry and hierarchical clustering

    Energy Technology Data Exchange (ETDEWEB)

    Li Tingting; Dai Ling; Li Lun; Hu Xuejiao; Dong Linjie; Li Jianjian; Salim, Sule Khalfan; Fu Jieying [Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, Hubei 430079 (China); Zhong Hongying, E-mail: hyzhong@mail.ccnu.edu.cn [Key Laboratory of Pesticides and Chemical Biology, Ministry of Education, College of Chemistry, Central China Normal University, Wuhan, Hubei 430079 (China)

    2011-01-17

    Rapid identification of unknown microorganisms of clinical and agricultural importance is not only critical for accurate diagnosis of infections but also essential for appropriate and prompt treatment. We describe here a rapid method for microorganisms typing based on quantitative analysis of fatty acids by iFAT approach (Isotope-coded Fatty Acid Transmethylation). In this work, lyophilized cell lysates were directly mixed with 0.5 M NaOH solution in d3-methanol and n-hexane. After 1 min of ultrasonication, the top n-hexane layer was combined with a mixture of standard d0-methanol derived fatty acid methylesters with known concentration. Measurement of intensity ratios of d3/d0 labeled fragment ion and molecular ion pairs at the corresponding target fatty acids provides a quantitative basis for hierarchical clustering. In the resultant dendrogram, the Euclidean distance between unknown species and known species quantitatively reveals their differences or shared similarities in fatty acid related pathways. It is of particular interest to apply this method for typing fungal species because fungi has distinguished lipid biosynthetic pathways that have been targeted for lots of drugs or fungicides compared with bacteria and animals. The proposed method has no dependence on the availability of genome or proteome databases. Therefore, it is can be applicable for a broad range of unknown microorganisms or mutant species.

  17. Formation of an O-Star Cluster by Hierarchical Accretion in G20.08-0.14 N

    CERN Document Server

    Galván-Madrid, Roberto; Zhang, Qizhou; Kurtz, Stan; Rodríguez, Luis F; Ho, Paul T P

    2009-01-01

    Spectral line and continuum observations of the ionized and molecular gas in G20.08-0.14 N explore the dynamics of accretion over a range of spatial scales in this massive star forming region. Very Large Array observations of NH_3 at 4'' angular resolution show a large scale (0.5 pc) molecular accretion flow around and into a star cluster with three small, bright HII regions. Higher resolution (0.4'') observations with the Submillimeter Array in hot core molecules (CH_3CN, OCS, and SO_2) and the VLA in NH_3, show that the two brightest and smallest HII regions are themselves surrounded by smaller scale (0.05 pc) accretion flows. The axes of rotation of the large and small scale flows are aligned, and the time scale for the contraction of the cloud is short enough, 0.1 Myr, for the large scale accretion flow to deliver significant mass to the smaller scales within the star formation time scale. The flow structure appears to be continuous and hierarchical from larger to smaller scales. Millimeter radio recombin...

  18. Hierarchical Network Design

    DEFF Research Database (Denmark)

    Thomadsen, Tommy

    2005-01-01

    Communication networks are immensely important today, since both companies and individuals use numerous services that rely on them. This thesis considers the design of hierarchical (communication) networks. Hierarchical networks consist of layers of networks and are well-suited for coping...... the clusters. The design of hierarchical networks involves clustering of nodes, hub selection, and network design, i.e. selection of links and routing of ows. Hierarchical networks have been in use for decades, but integrated design of these networks has only been considered for very special types of networks....... The thesis investigates models for hierarchical network design and methods used to design such networks. In addition, ring network design is considered, since ring networks commonly appear in the design of hierarchical networks. The thesis introduces hierarchical networks, including a classification scheme...

  19. Longitudinal Analyses of a Hierarchical Model of Peer Social Competence for Preschool Children: Structural Fidelity and External Correlates

    Science.gov (United States)

    Shin, Nana; Vaughn, Brian E.; Kim, Mina; Krzysik, Lisa; Bost, Kelly K.; McBride, Brent; Santos, Antonio J.; Peceguina, Ines; Coppola, Gabrielle

    2011-01-01

    Achieving consensus on the definition and measurement of social competence (SC) for preschool children has proven difficult in the developmental sciences. We tested a hierarchical model in which SC is assumed to be a second-order latent variable by using longitudinal data (N = 345). We also tested the degree to which peer SC at Time 1 predicted…

  20. Classifying airborne radiometry data with Agglomerative Hierarchical Clustering: A tool for geological mapping in context of rainforest (French Guiana)

    Science.gov (United States)

    Martelet, G.; Truffert, C.; Tourlière, B.; Ledru, P.; Perrin, J.

    2006-09-01

    In highly weathered environments, it is crucial that geological maps provide information concerning both the regolith and the bedrock, for societal needs, such as land-use, mineral or water resources management. Often, geologists are facing the challenge of upgrading existing maps, as relevant information concerning weathering processes and pedogenesis is currently missing. In rugged areas in particular, where access to the field is difficult, ground observations are sparsely available, and need therefore to be complemented using methods based on remotely sensed data. For this purpose, we discuss the use of Agglomerative Hierarchical Clustering (AHC) on eU, K and eTh airborne gamma-ray spectrometry grids. The AHC process allows primarily to segment the geophysical maps into zones having coherent U, K and Th contents. The analysis of these contents are discussed in terms of geochemical signature for lithological attribution of classes, as well as the use of a dendrogram, which gives indications on the hierarchical relations between classes. Unsupervised classification maps resulting from AHC can be considered as spatial models of the distribution of the radioelement content in surface and sub-surface formations. The source of gamma rays emanating from the ground is primarily related to the geochemistry of the bedrock and secondarily to modifications of the radioelement distribution by weathering and other secondary mechanisms, such as mobilisation by wind or water. The interpretation of the obtained predictive classified maps, their U, K, Th contents, and the dendrogram, in light of available geological knowledge, allows to separate signatures related to regolith and solid geology. Consequently, classification maps can be integrated within a GIS environment and used by the geologist as a support for mapping bedrock lithologies and their alteration. We illustrate the AHC classification method in the region of Cayenne using high-resolution airborne radiometric data

  1. Comparing chemistry to outcome: the development of a chemical distance metric, coupled with clustering and hierarchal visualization applied to macromolecular crystallography.

    Directory of Open Access Journals (Sweden)

    Andrew E Bruno

    Full Text Available Many bioscience fields employ high-throughput methods to screen multiple biochemical conditions. The analysis of these becomes tedious without a degree of automation. Crystallization, a rate limiting step in biological X-ray crystallography, is one of these fields. Screening of multiple potential crystallization conditions (cocktails is the most effective method of probing a proteins phase diagram and guiding crystallization but the interpretation of results can be time-consuming. To aid this empirical approach a cocktail distance coefficient was developed to quantitatively compare macromolecule crystallization conditions and outcome. These coefficients were evaluated against an existing similarity metric developed for crystallization, the C6 metric, using both virtual crystallization screens and by comparison of two related 1,536-cocktail high-throughput crystallization screens. Hierarchical clustering was employed to visualize one of these screens and the crystallization results from an exopolyphosphatase-related protein from Bacteroides fragilis, (BfR192 overlaid on this clustering. This demonstrated a strong correlation between certain chemically related clusters and crystal lead conditions. While this analysis was not used to guide the initial crystallization optimization, it led to the re-evaluation of unexplained peaks in the electron density map of the protein and to the insertion and correct placement of sodium, potassium and phosphate atoms in the structure. With these in place, the resulting structure of the putative active site demonstrated features consistent with active sites of other phosphatases which are involved in binding the phosphoryl moieties of nucleotide triphosphates. The new distance coefficient, CDcoeff, appears to be robust in this application, and coupled with hierarchical clustering and the overlay of crystallization outcome, reveals information of biological relevance. While tested with a single example the

  2. Hierarchical multiple bit clusters and patterned media enabled by novel nanofabrication techniques -- High resolution electron beam lithography and block polymer self assembly

    Science.gov (United States)

    Xiao, Qijun

    This thesis discusses the full scope of a project exploring the physics of hierarchical clusters of interacting nanomagnets. These clusters may be relevant for novel applications such as multilevel data storage devices. The work can be grouped into three main activities: micromagnetic simulation, fabrication and characterization of proof-of-concept prototype devices, and efforts to scale down the structures by creating the hierarchical structures with the aid of diblock copolymer self assembly. Theoretical micromagnetic studies and simulations based on Landau-Lifshitz-Gilbert (LLG) equation were conducted on nanoscale single domain magnetic entities. For the simulated nanomagnet clusters with perpendicular uniaxial anisotropy, the simulation showed the switching field distributions, the stability of the magnetostatic states with distinctive total cluster perpendicular moments, and the stepwise magnetic switching curves. For simulated nanomagnet clusters with in-plane shape anisotropy, the simulation showed the stepwise switching behaviors governed by thermal agitation and cluster configurations. Proof-of-concept cluster devices with three interacting Co nanomagnets were fabricated by e-beam lithography (EBL) and pulse-reverse electrochemical deposition (PRECD). EBL patterning on a suspended 100 nm SiN membrane showed improved lateral lithography resolution to 30 nm. The Co nanomagnets deposited using the PRECD method showed perpendicular anisotropy. The switching experiments with external applied fields were able to switch the Co nanomagnets through the four magnetostatic states with distinctive total perpendicular cluster magnetization, and proved the feasibility of multilevel data storage devices based on the cluster concept. Shrinking the structures size was experimented by the aid of diblock copolymer. Thick poly(styrene)-b-poly(methyl methacrylate) (PS-b-PMMA) diblock copolymer templates aligned with external electrical field were used to fabricate long Ni

  3. A Parallel Multigrid Solver for High Frequency Electromagnetic Field Analyses with Small-scale PC Cluster

    Science.gov (United States)

    Yosui, Kuniaki; Iwashita, Takeshi; Mori, Michiya; Kobayashi, Eiichi

    Finite element analyses of electromagnetic field are commonly used for designing of various electronic devices. The scale of the analyses becomes larger and larger, therefore, a fast linear solver is needed to solve linear equations arising from the finite element method. Since a multigrid solver is the fastest linear solver for these problems, parallelization of a multigrid solver is a quite useful approach. From the viewpoint of industrial applications, an effective usage of a small-scale PC cluster is important due to initial cost for introducing parallel computers. In this paper, a distributed parallel multigrid solver for a small-scale PC cluster is developed. In high frequency electromagnetic field analyses, a special block Gauss-Seidel smoother is used for the multigrid solver instead of general smoothers such as Gauss-Seidel smoother or Jacobi smoother in order to improve a convergence rate. The block multicolor ordering technique is applied to parallelize the smoother. A numerical exsample shows that a 3.7-fold speed-up in computational time and a 3.0-fold increase in the scale of the analysis were attained when the number of CPU was increased from one to five.

  4. A Performance-Prediction Model for PIC Applications on Clusters of Symmetric MultiProcessors: Validation with Hierarchical HPF+OpenMP Implementation

    Directory of Open Access Journals (Sweden)

    Sergio Briguglio

    2003-01-01

    Full Text Available A performance-prediction model is presented, which describes different hierarchical workload decomposition strategies for particle in cell (PIC codes on Clusters of Symmetric MultiProcessors. The devised workload decomposition is hierarchically structured: a higher-level decomposition among the computational nodes, and a lower-level one among the processors of each computational node. Several decomposition strategies are evaluated by means of the prediction model, with respect to the memory occupancy, the parallelization efficiency and the required programming effort. Such strategies have been implemented by integrating the high-level languages High Performance Fortran (at the inter-node stage and OpenMP (at the intra-node one. The details of these implementations are presented, and the experimental values of parallelization efficiency are compared with the predicted results.

  5. Comparison of multianalyte proficiency test results by sum of ranking differences, principal component analysis, and hierarchical cluster analysis.

    Science.gov (United States)

    Škrbić, Biljana; Héberger, Károly; Durišić-Mladenović, Nataša

    2013-10-01

    Sum of ranking differences (SRD) was applied for comparing multianalyte results obtained by several analytical methods used in one or in different laboratories, i.e., for ranking the overall performances of the methods (or laboratories) in simultaneous determination of the same set of analytes. The data sets for testing of the SRD applicability contained the results reported during one of the proficiency tests (PTs) organized by EU Reference Laboratory for Polycyclic Aromatic Hydrocarbons (EU-RL-PAH). In this way, the SRD was also tested as a discriminant method alternative to existing average performance scores used to compare mutlianalyte PT results. SRD should be used along with the z scores--the most commonly used PT performance statistics. SRD was further developed to handle the same rankings (ties) among laboratories. Two benchmark concentration series were selected as reference: (a) the assigned PAH concentrations (determined precisely beforehand by the EU-RL-PAH) and (b) the averages of all individual PAH concentrations determined by each laboratory. Ranking relative to the assigned values and also to the average (or median) values pointed to the laboratories with the most extreme results, as well as revealed groups of laboratories with similar overall performances. SRD reveals differences between methods or laboratories even if classical test(s) cannot. The ranking was validated using comparison of ranks by random numbers (a randomization test) and using seven folds cross-validation, which highlighted the similarities among the (methods used in) laboratories. Principal component analysis and hierarchical cluster analysis justified the findings based on SRD ranking/grouping. If the PAH-concentrations are row-scaled, (i.e., z scores are analyzed as input for ranking) SRD can still be used for checking the normality of errors. Moreover, cross-validation of SRD on z scores groups the laboratories similarly. The SRD technique is general in nature, i.e., it can

  6. Identification of Clinical Phenotypes Using Cluster Analyses in COPD Patients with Multiple Comorbidities

    Directory of Open Access Journals (Sweden)

    Pierre-Régis Burgel

    2014-01-01

    Full Text Available Chronic obstructive pulmonary disease (COPD is characterized by persistent airflow limitation, the severity of which is assessed using forced expiratory volume in 1 sec (FEV1, % predicted. Cohort studies have confirmed that COPD patients with similar levels of airflow limitation showed marked heterogeneity in clinical manifestations and outcomes. Chronic coexisting diseases, also called comorbidities, are highly prevalent in COPD patients and likely contribute to this heterogeneity. In recent years, investigators have used innovative statistical methods (e.g., cluster analyses to examine the hypothesis that subgroups of COPD patients sharing clinically relevant characteristics (phenotypes can be identified. The objectives of the present paper are to review recent studies that have used cluster analyses for defining phenotypes in observational cohorts of COPD patients. Strengths and weaknesses of these statistical approaches are briefly described. Description of the phenotypes that were reasonably reproducible across studies and received prospective validation in at least one study is provided, with a special focus on differences in age and comorbidities (including cardiovascular diseases. Finally, gaps in current knowledge are described, leading to proposals for future studies.

  7. Cluster and principle component analyses of maize accessions under normal and water stress conditions

    Directory of Open Access Journals (Sweden)

    Mustafa Hafiz Saad Bin

    2015-01-01

    Full Text Available In the current set of an experiment, forty maize genotypes were assessed for drought associated traits. For evaluation of these traits, PC and correlation analyses were employed to obtain suitable parents that can be further exploited in future breeding programmes. Correlation analysis revealed some important associations among the traits studied. Fresh root length had positive and significant associations, but leaf temperature had a significant negative correlation with root density at both 40% and 100% moisture levels while root density had negative association at 100% and positive correlation at 40% moisture level with chlorophyll content. The positive correlation among these yield contributing traits suggested that these characters are important for direct selection of drought tolerant high yielding genotypes. Principal component (PC analysis showed first 4 PCs having Eigen value >1 explaining 86.7% and 88.4% of the total variation at 40% and 100% moisture levels respectively with different drought related traits. Cluster analysis classified 40 accessions into four divergent groups. The members of clusters 1 and 2 may be combined in future breeding programmes to obtain genotypes/hybrids that can perform well under drought stress conditions. Members of cluster 3 may be selected on the basis of root density, leaf temperature, dry root weight and root shoot ratio by weight and can be combined with members of cluster 4 due to higher leaf temperature and root shoot ratio by length. The results showed that the germplasm having a wide genetic diversity can be thus utilized for future breeding programme to obtain drought tolerant maize genotypes/ hybrids for adaptation to water scarce areas.

  8. A general strategy to determine the congruence between a hierarchical and a non-hierarchical classification

    Directory of Open Access Journals (Sweden)

    Marín Ignacio

    2007-11-01

    Full Text Available Abstract Background Classification procedures are widely used in phylogenetic inference, the analysis of expression profiles, the study of biological networks, etc. Many algorithms have been proposed to establish the similarity between two different classifications of the same elements. However, methods to determine significant coincidences between hierarchical and non-hierarchical partitions are still poorly developed, in spite of the fact that the search for such coincidences is implicit in many analyses of massive data. Results We describe a novel strategy to compare a hierarchical and a dichotomic non-hierarchical classification of elements, in order to find clusters in a hierarchical tree in which elements of a given "flat" partition are overrepresented. The key improvement of our strategy respect to previous methods is using permutation analyses of ranked clusters to determine whether regions of the dendrograms present a significant enrichment. We show that this method is more sensitive than previously developed strategies and how it can be applied to several real cases, including microarray and interactome data. Particularly, we use it to compare a hierarchical representation of the yeast mitochondrial interactome and a catalogue of known mitochondrial protein complexes, demonstrating a high level of congruence between those two classifications. We also discuss extensions of this method to other cases which are conceptually related. Conclusion Our method is highly sensitive and outperforms previously described strategies. A PERL script that implements it is available at http://www.uv.es/~genomica/treetracker.

  9. Infrared High-Resolution Integrated Light Spectral Analyses of M31 Globular Clusters from APOGEE

    CERN Document Server

    Sakari, Charli M; Schiavon, Ricardo P; Bizyaev, Dmitry; Prieto, Carlos Allende; Beers, Timothy C; Caldwell, Nelson; Garcia-Hernandez, Domingo Anibal; Lucatello, Sara; Majewski, Steven; O'Connell, Robert W; Pan, Kaike; Strader, Jay

    2016-01-01

    Chemical abundances are presented for 25 M31 globular clusters (GCs), based on moderately high resolution (R = 22, 500) H-band integrated light spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). Infrared spectra offer lines from new elements, of different strengths, and at higher excitation potentials compared to the optical. Integrated abundances of C, N, and O are derived from CO, CN, and OH molecular features, while Fe, Na, Mg, Al, Si, K, Ca, and Ti abundances are derived from atomic features. These abundances are compared to previous results from the optical, demonstrating the validity and value of infrared integrated light analyses. The CNO abundances are consistent with typical tip of the red giant branch stellar abundances, but are systematically offset from optical, Lick index abundances. With a few exceptions, the other abundances agree between the optical and the infrared within the 1{\\sigma} uncertainties. The first integrated K abundances are also presented, and demo...

  10. Proportion and cluster analyses of the skull in various species of the tree shrews.

    Science.gov (United States)

    Endo, Hideki; Hikida, Tsutomu; Chou, Loke Ming; Fukuta, Katsuhiro; Stafford, Brian J

    2004-01-01

    The skull adaptation was functional-morphologically examined in 14 species of the tree shrews. From the data of the proportion indices, the similarities were confirmed between T. minor and T. gracilis, T. tana and T. dorsalis, and T. longipes and T. glis. We demonstrated that the splanchnocranium was elongated in terrestrial T. tana and T. dorsalis and shortened in arboreal T. minor and T. gracilis from the proportion data. In both dendrogram from the matrix of the Q-mode correlation coefficients and scattergram from the canonical discriminant analysis, the morphological similarities in the skull shape suggested the terrestrial-insectivorous adaptation of T. tana and T. dorsalis, and the arboreal adaptation of T. minor and T. gracilis. Since the osteometrical skull similarities were indicated among the three species of Tupaia by cluster and canonical discriminant analyses, the arbo-terrestrial behavior and its functional-morphological adaptation may be commonly established in T. montana, T. longipes and T. glis.

  11. Gene-Set Local Hierarchical Clustering (GSLHC--A Gene Set-Based Approach for Characterizing Bioactive Compounds in Terms of Biological Functional Groups.

    Directory of Open Access Journals (Sweden)

    Feng-Hsiang Chung

    Full Text Available Gene-set-based analysis (GSA, which uses the relative importance of functional gene-sets, or molecular signatures, as units for analysis of genome-wide gene expression data, has exhibited major advantages with respect to greater accuracy, robustness, and biological relevance, over individual gene analysis (IGA, which uses log-ratios of individual genes for analysis. Yet IGA remains the dominant mode of analysis of gene expression data. The Connectivity Map (CMap, an extensive database on genomic profiles of effects of drugs and small molecules and widely used for studies related to repurposed drug discovery, has been mostly employed in IGA mode. Here, we constructed a GSA-based version of CMap, Gene-Set Connectivity Map (GSCMap, in which all the genomic profiles in CMap are converted, using gene-sets from the Molecular Signatures Database, to functional profiles. We showed that GSCMap essentially eliminated cell-type dependence, a weakness of CMap in IGA mode, and yielded significantly better performance on sample clustering and drug-target association. As a first application of GSCMap we constructed the platform Gene-Set Local Hierarchical Clustering (GSLHC for discovering insights on coordinated actions of biological functions and facilitating classification of heterogeneous subtypes on drug-driven responses. GSLHC was shown to tightly clustered drugs of known similar properties. We used GSLHC to identify the therapeutic properties and putative targets of 18 compounds of previously unknown characteristics listed in CMap, eight of which suggest anti-cancer activities. The GSLHC website http://cloudr.ncu.edu.tw/gslhc/ contains 1,857 local hierarchical clusters accessible by querying 555 of the 1,309 drugs and small molecules listed in CMap. We expect GSCMap and GSLHC to be widely useful in providing new insights in the biological effect of bioactive compounds, in drug repurposing, and in function-based classification of complex diseases.

  12. Single-cell expression analyses during cellular reprogramming reveal an early stochastic and a late hierarchic phase

    NARCIS (Netherlands)

    Buganim, Y.; Faddah, D.A.; Cheng, A.W.; Itskovich, E.; Markoulaki, S.; Ganz, K.; Klemm, S.L.; van Oudenaarden, A.; Jaenisch, R.

    2012-01-01

    During cellular reprogramming, only a small fraction of cells become induced pluripotent stem cells (iPSCs). Previous analyses of gene expression during reprogramming were based on populations of cells, impeding single-cell level identification of reprogramming events. We utilized two gene

  13. Analysing star cluster populations with stochastic models: the HST/WFC3 sample of clusters in M83

    CERN Document Server

    Fouesneau, Morgan; Chandar, Rupali; Whitmore, Bradley C

    2012-01-01

    The majority of clusters in the Universe have masses well below 10^5 Msun. Hence their integrated fluxes and colors can be affected by the random presence of a few bright stars introduced by stochastic sampling of the stellar mass function. Specific methods are being developed to extend the analysis of cluster SEDs into the low-mass regime. In this paper, we apply such a method to observations of star clusters, in the nearby spiral galaxy M83. We reassess ages and masses of a sample of 1242 objects for which UBVIHalpha fluxes were obtained with the HST/WFC3 images. Synthetic clusters with known properties are used to characterize the limitations of the method. The ensemble of color predictions of the discrete cluster models are in good agreement with the distribution of observed colors. We emphasize the important role of the Halpha data in the assessment of the fraction of young objects, particularly in breaking the age-extinction degeneracy that hampers an analysis based on UBVI only. We find the mass distri...

  14. HYPODD Relocations and Stress Tensor Inversion Analyses of Local Earthquake Clusters in the Sea of Marmara

    Science.gov (United States)

    Korkusuz Öztürk, Yasemin; Meral Özel, Nurcan

    2016-04-01

    Extensional focal mechanism solutions are mostly observed even in the Central Marmara by this comprehensive research although the main Marmara Fault that is the western branch of the NAF, is dominated by a right lateral strike-slip regime. Marmara Region, a seismically very active area, is located at the western section of the North Anatolian Fault Zone (NAFZ). The 1912 Mürefte and 1999 Izmit earthquakes are the last devastating events of the western and eastern sections of this region, respectively. The region between the locations of these earthquakes, is prone to a large earthquake. Therefore, the analysis of the Sea of Marmara is significant. The main objective of this research is to determine earthquake hypocenters and focal mechanism solutions accurately, hence we obtain recent states of stresses for this region. Accordingly, this research aims to define branches of fault structures and its geometrical orientations in the Sea of Marmara. In this study, a cluster of events in the Central Marmara is analyzed using hypocenter program as a usual location technique. In addition, these events and other clustered events (Korkusuz Öztürk et al., 2015) are relocated using HYPODD relocation procedure. Even though NAF is mostly dominated by a right lateral strike slip fault, we found out many extensional source mechanisms. Also, from the comparison of relocation results of hypocenter and HYPODD programs, it is found out that most of the relocations have the same orientations and dipping angles of the segments of the main Marmara Fault are not clear. As a result, since we observe many normal faulting mechanisms in the Sea of Marmara, we expect to observe some deviations in orientations of vertical orientations of the fault segments comparing a dip-slip model. Therefore, this research will continue to clearly identify fault dip angles of main fault segments in Marmara Sea. Further, our sensitive relocation and stress analyses will make an important contribution to a

  15. Double feature selection and cluster analyses in mining of microarray data from cotton

    Directory of Open Access Journals (Sweden)

    Wilkins Thea A

    2008-06-01

    Full Text Available Abstract Background Cotton fiber is a single-celled seed trichome of major biological and economic importance. In recent years, genomic approaches such as microarray-based expression profiling were used to study fiber growth and development to understand the developmental mechanisms of fiber at the molecular level. The vast volume of microarray expression data generated requires a sophisticated means of data mining in order to extract novel information that addresses fundamental questions of biological interest. One of the ways to approach microarray data mining is to increase the number of dimensions/levels to the analysis, such as comparing independent studies from different genotypes. However, adding dimensions also creates a challenge in finding novel ways for analyzing multi-dimensional microarray data. Results Mining of independent microarray studies from Pima and Upland (TM1 cotton using double feature selection and cluster analyses identified species-specific and stage-specific gene transcripts that argue in favor of discrete genetic mechanisms that govern developmental programming of cotton fiber morphogenesis in these two cultivated species. Double feature selection analysis identified the highest number of differentially expressed genes that distinguish the fiber transcriptomes of developing Pima and TM1 fibers. These results were based on the finding that differences in fibers harvested between 17 and 24 day post-anthesis (dpa represent the greatest expressional distance between the two species. This powerful selection method identified a subset of genes expressed during primary (PCW and secondary (SCW cell wall biogenesis in Pima fibers that exhibits an expression pattern that is generally reversed in TM1 at the same developmental stage. Cluster and functional analyses revealed that this subset of genes are primarily regulated during the transition stage that overlaps the termination of PCW and onset of SCW biogenesis, suggesting

  16. Orientation Bias of Optically Selected Galaxy Clusters and Its Impact on Stacked Weak Lensing Analyses

    CERN Document Server

    Dietrich, Jörg P; Song, Jeeseon; McKay, Christopher P Davis Timothy A; Baruah, Leon; Becker, Matthew; Benoist, Christophe; Busha, Michael; da Costa, Luiz A N; Hao, Jiangang; Maia, Marcio A G; Miller, Christopher J; Ogando, Ricardo; Romer, A Kathy; Rozo, Eduardo; Rykoff, Eli; Wechsler, Risa

    2014-01-01

    Weak-lensing measurements of the averaged shear profiles of galaxy clusters binned by some proxy for cluster mass are commonly converted to cluster mass estimates under the assumption that these cluster stacks have spherical symmetry. In this paper we test whether this assumption holds for optically selected clusters binned by estimated optical richness. Using mock catalogues created from N-body simulations populated realistically with galaxies, we ran a suite of optical cluster finders and estimated their optical richness. We binned galaxy clusters by true cluster mass and estimated optical richness and measure the ellipticity of these stacks. We find that the processes of optical cluster selection and richness estimation are biased, leading to stacked structures that are elongated along the line-of-sight. We show that weak-lensing alone cannot measure the size of this orientation bias. Weak lensing masses of stacked optically selected clusters are overestimated by up to 3-6 per cent when clusters can be uni...

  17. Environmental quenching and hierarchical cluster assembly: Evidence from spectroscopic ages of red-sequence galaxies in Coma

    CERN Document Server

    Smith, Russell J; Price, James; Hudson, Michael J; Phillipps, Steven

    2011-01-01

    We explore the variation in stellar population ages for Coma cluster galaxies as a function of projected cluster-centric distance, using a sample of 362 red-sequence galaxies with high signal-to-noise spectroscopy. The sample spans a wide range in luminosity (0.02-4 L*) and extends from the cluster core to near the virial radius. We find a clear distinction in the observed trends of the giant and dwarf galaxies. The ages of red-sequence giants are primarily determined by galaxy mass, with only weak modulation by environment, in the sense that galaxies at larger cluster-centric distance are slightly younger. For red-sequence dwarfs (with mass <10^10 Msun), the roles of mass and environment as predictors of age are reversed: there is little dependence on mass, but strong trends with projected cluster-centric radius are observed. The average age of dwarfs at the 2.5 Mpc limit of our sample is approximately half that of dwarfs near the cluster centre. The gradient in dwarf galaxy ages is a global cluster-centr...

  18. Infrared High-resolution Integrated Light Spectral Analyses of M31 Globular Clusters from APOGEE

    Science.gov (United States)

    Sakari, Charli M.; Shetrone, Matthew D.; Schiavon, Ricardo P.; Bizyaev, Dmitry; Allende Prieto, Carlos; Beers, Timothy C.; Caldwell, Nelson; Aníbal García-Hernández, Domingo; Lucatello, Sara; Majewski, Steven; O'Connell, Robert W.; Pan, Kaike; Strader, Jay

    2016-10-01

    Chemical abundances are presented for 25 M31 globular clusters (GCs), based on moderately high resolution (R = 22,500) H-band integrated light (IL) spectra from the Apache Point Observatory Galactic Evolution Experiment (APOGEE). Infrared (IR) spectra offer lines from new elements, lines of different strengths, and lines at higher excitation potentials compared to the optical. Integrated abundances of C, N, and O are derived from CO, CN, and OH molecular features, while Fe, Na, Mg, Al, Si, K, Ca, and Ti abundances are derived from atomic features. These abundances are compared to previous results from the optical, demonstrating the validity and value of IR IL analyses. The CNO abundances are consistent with typical tip of the red giant branch stellar abundances but are systematically offset from optical Lick index abundances. With a few exceptions, the other abundances agree between the optical and the IR within the 1σ uncertainties. The first integrated K abundances are also presented and demonstrate that K tracks the α elements. The combination of IR and optical abundances allows better determinations of GC properties and enables probes of the multiple populations in extragalactic GCs. In particular, the integrated effects of the Na/O anticorrelation can be directly examined for the first time.

  19. Hierarchical rutile TiO2 flower cluster-based high efficiency dye-sensitized solar cells via direct hydrothermal growth on conducting substrates.

    Science.gov (United States)

    Ye, Meidan; Liu, Hsiang-Yu; Lin, Changjian; Lin, Zhiqun

    2013-01-28

    Dye-sensitized solar cells (DSSCs) based on hierarchical rutile TiO(2) flower clusters prepared by a facile, one-pot hydrothermal process exhibit a high efficiency. Complex yet appealing rutile TiO(2) flower films are, for the first time, directly hydrothermally grown on a transparent conducting fluorine-doped tin oxide (FTO) substrate. The thickness and density of as-grown flower clusters can be readily tuned by tailoring growth parameters, such as growth time, the addition of cations of different valence and size, initial concentrations of precursor and cation, growth temperature, and acidity. Notably, the small lattice mismatch between the FTO substrate and rutile TiO(2) renders the epitaxial growth of a compact rutile TiO(2) layer on the FTO glass. Intriguingly, these TiO(2) flower clusters can then be exploited as photoanodes to produce DSSCs, yielding a power conversion efficiency of 2.94% despite their rutile nature, which is further increased to 4.07% upon the TiCl(4) treatment.

  20. Genetic and economic analyses of sow replacement rates in the commercial tier of a hierarchical swine breeding structure.

    Science.gov (United States)

    Faust, M A; Robison, O W; Tess, M W

    1993-06-01

    Commercial-level sow replacement rates were investigated for a 10-yr planning horizon using a stochastic life-cycle swine production model. A three-tiered breeding structure was modeled for the production of market hogs in a three-breed static crossing scheme. Growth and reproductive traits of individual pigs were simulated using genetic, environmental, and economic parameters. Culling was after a maximum of 1, 5, or 10 parities in commercial levels within 1- and 5-parity nucleus and 1-, 5-, and 10-parity multiplier combinations. Yearly changes and average phenotypic levels were computed for pig and sow performance and economic measures. For growth traits, greater commercial level response was for systems with higher sow replacement rates, 110 to 115% of lowest response. Phenotypic changes in net returns ranged from $.85 to 1.01 x pig-1 x yr-1. Average growth performances were highest for systems with greatest genetic trend. Highest kilograms.sow-1 x year-1 finished was for 10-parity commercial alternatives. System differences in total costs and returns per pig resulted primarily from differences in replacement costs. Removal of the gilt system from analyses often reduced ranges among systems for economic measures by more than 70%. Systems with the lowest commercial replacement rates were most profitable. Within these systems, those with higher genetic change had highest net returns. For high replacement rates, no more than 175% of market value could be paid for gilts, but with lower sow replacement rates commercial units could justify as much as 450%.

  1. 一种层次聚类的RDF图语义检索方法研究%Hierarchical clustering-based semantic retrieval of RDF graph

    Institute of Scientific and Technical Information of China (English)

    刘宁; 左凤华; 张俊

    2012-01-01

    The cun-ent research related RDF graph retrieve exists some problems, such as low efficiency of memory usage, low search efficiency and so on. This paper proposed a hierarchical clustering semantic retrieval model on RDF graph and the method based on the model to solve aforesaid problems. That extracting entities from RDF graph and hierarchical clustering by the guidance of the ontology library made the complex graph structure into a tree structure for efficient retrieval. Orientating target object which was one of nodes in the model in RDF conducted the semantic expansion queries. Retrieval efficiency increased because retrieval scope narrow down as construction of retrieval model and recall ratio increased by the semantic expansion queries.%针对当前信息资源描述框架(RDF)检索过程中存在的内存使用过大及检索效率低等问题,提出一个RDF图的层次聚类语义检索模型,设计并实现了相应的检索方法.首先从RDF图中抽取实体数据,在本体库的指导下,通过层次聚类,将复杂的图形结构转换为适合检索的树型结构;根据在树中查找到的目标对象,确定其在RDF图中的位置,进行语义扩充查询.检索模型的构建缩小了检索范围,从而提高了检索效率,其语义扩充查询还可以得到较好的查全率.

  2. 基于类轮廓层次聚类方法的研究%RESEARCH ON CLASS-PROFILE-BASED HIERARCHICAL CLUSTERING METHOD

    Institute of Scientific and Technical Information of China (English)

    孟海东; 唐旋

    2011-01-01

    传统的聚类算法在考虑类与类之间的连通性特征和近似性特征上往往顾此失彼.首先给出类边界点和类轮廓的基本定义以及寻求方法,然后基于类间连通性特征和近似性特征的综合考虑,拟定一些类间相似性度量标准和方法,最后提出一种基于类轮廓的层次聚类算法.该算法能够有效处理任意形状的簇,且能够区分孤立点和噪声数据.通过对图像数据集和Iris标准数据集的聚类分析,验证了该算法的可行性和有效性.%Traditional clustering algorithms are often incapable of roundly considering the connectivity and similarity characteristics among classes. The thesis firstly presents the fundamental definition of class boundary point and class profile; secondly, with comprehensive consideration based on connectivity characteristics and similarity characteristics among classes, defines some standards and methods for inter class similarity measurement; thirdly, proposes a class-profile-based hierarchical clustering algorithm, which is able to effectively process arbitrary shaped clusters and distinguish isolated points from noise data. The feasibility and effectiveness of the algorithm is validated through clustering analysis on image data sets and Iris standard data sets.

  3. Ultrathin mesoporous Co3O4 nanosheets-constructed hierarchical clusters as high rate capability and long life anode materials for lithium-ion batteries

    Science.gov (United States)

    Wu, Shengming; Xia, Tian; Wang, Jingping; Lu, Feifei; Xu, Chunbo; Zhang, Xianfa; Huo, Lihua; Zhao, Hui

    2017-06-01

    Herein, Ultrathin mesoporous Co3O4 nanosheets-constructed hierarchical clusters (UMCN-HCs) have been successfully synthesized via a facile hydrothermal method followed by a subsequent thermolysis treatment at 600 °C in air. The products consist of cluster-like Co3O4 microarchitectures, which are assembled by numerous ultrathin mesoporous Co3O4 nanosheets. When tested as anode materials for lithium-ion batteries, UMCN-HCs deliver a high reversible capacity of 1067 mAh g-1 at a current density of 100 mA g-1 after 100 cycles. Even at 2 A g-1, a stable capacity as high as 507 mAh g-1 can be achieved after 500 cycles. The high reversible capacity, excellent cycling stability, and good rate capability of UMCN-HCs may be attributed to their mesoporous sheet-like nanostructure. The sheet-layered structure of UMCN-HCs may buffer the volume change during the lithiation-delithiation process, and the mesoporous characteristic make lithium-ion transfer more easily at the interface between the active electrode and the electrolyte.

  4. Fingerprint analysis of Hibiscus mutabilis L. leaves based on ultra performance liquid chromatography with photodiode array detector combined with similarity analysis and hierarchical clustering analysis methods

    Directory of Open Access Journals (Sweden)

    Xianrui Liang

    2013-01-01

    Full Text Available Background: A method for chemical fingerprint analysis of Hibiscus mutabilis L. leaves was developed based on ultra performance liquid chromatography with photodiode array detector (UPLC-PAD combined with similarity analysis (SA and hierarchical clustering analysis (HCA. Materials and Methods: 10 batches of Hibiscus mutabilis L. leaves samples were collected from different regions of China. UPLC-PAD was employed to collect chemical fingerprints of Hibiscus mutabilis L. leaves. Results: The relative standard deviations (RSDs of the relative retention times (RRT and relative peak areas (RPA of 10 characteristic peaks (one of them was identified as rutin in precision, repeatability and stability test were less than 3%, and the method of fingerprint analysis was validated to be suitable for the Hibiscus mutabilis L. leaves. Conclusions: The chromatographic fingerprints showed abundant diversity of chemical constituents qualitatively in the 10 batches of Hibiscus mutabilis L. leaves samples from different locations by similarity analysis on basis of calculating the correlation coefficients between each two fingerprints. Moreover, the HCA method clustered the samples into four classes, and the HCA dendrogram showed the close or distant relations among the 10 samples, which was consistent to the SA result to some extent.

  5. Investigating the provenance of iron artifacts of the Royal Iron Factory of Sao Joao de Ipanema by hierarchical cluster analysis of EDS microanalyses of slag inclusions

    Energy Technology Data Exchange (ETDEWEB)

    Mamani-Calcina, Elmer Antonio; Landgraf, Fernando Jose Gomes; Azevedo, Cesar Roberto de Farias, E-mail: c.azevedo@usp.br [Universidade de Sao Paulo (USP), Sao Paulo, SP (Brazil). Escola Politecnica. Departmento de Engenharia Metalurgica e de Materiais

    2017-01-15

    Microstructural characterization techniques, including EDX (Energy Dispersive X-ray Analysis) microanalyses, were used to investigate the slag inclusions in the microstructure of ferrous artifacts of the Royal Iron Factory of Sao Joao de Ipanema (first steel plant of Brazil, XIX century), the D. Pedro II Bridge (located in Bahia, assembled in XIX century and produced in Scotland) and the archaeological sites of Sao Miguel de Missoes (Rio Grande do Sul, Brazil, production site of iron artifacts, the XVIII century) and Afonso Sardinha (Sao Paulo, Brazil production site of iron artifacts, XVI century). The microanalyses results of the main micro constituents of the microstructure of the slag inclusions were investigated by hierarchical cluster analysis and the dendrogram with the microanalyses results of the wüstite phase (using as critical variables the contents of MnO, MgO, Al{sub 2}O{sub 3}, V{sub 2}O{sub 5} and TiO{sub 2}) allowed the identification of four clusters, which successfully represented the samples of the four investigated sites (Ipanema, Sardinha, Missoes and Bahia). Finally, the comparatively low volumetric fraction of slag inclusions in the samples of Ipanema (∼1%) suggested the existence of technological expertise at the iron making processing in the Royal Iron Factory of Sao Joao de Ipanema. (author)

  6. Dietary patterns in Irish adolescents: a comparison of cluster and principal component analyses.

    Science.gov (United States)

    Hearty, Áine P; Gibney, Michael J

    2013-05-01

    Pattern analysis of adolescent diets may provide an important basis for nutritional health promotion. The aims of the present study were to examine and compare dietary patterns in adolescents using cluster analysis and principal component analysis (PCA) and to examine the impact of the format of the dietary variables on the solutions. Analysis was based on the Irish National Teens Food Survey, in which food intake data were collected using a semi-quantitative 7 d food diary. Thirty-two food groups were created and were expressed as either g/d or percentage contribution to total energy. Dietary patterns were identified using cluster analysis (k-means) and PCA. Republic of Ireland, 2005-2006. A representative sample of 441 adolescents aged 13-17 years. Five clusters based on percentage contribution to total energy were identified, 'Healthy', 'Unhealthy', 'Rice/Pasta dishes', 'Sandwich' and 'Breakfast cereal & Main meal-type foods'. Four principal components based on g/d were identified which explained 28 % of total variance: 'Healthy foods', 'Traditional foods', 'Sandwich foods' and 'Unhealthy foods'. A 'Sandwich' and an 'Unhealthy' pattern are the main dietary patterns in this sample. Patterns derived from either cluster analysis or PCA were comparable, although it appears that cluster analysis also identifies dietary patterns not identified through PCA, such as a 'Breakfast cereal & Main meal-type foods' pattern. Consideration of the format of the dietary variable is important as it can directly impact on the patterns obtained for both cluster analysis and PCA.

  7. Comparison of Poisson and Bernoulli spatial cluster analyses of pediatric injuries in a fire district

    Directory of Open Access Journals (Sweden)

    Warden Craig R

    2008-09-01

    Full Text Available Abstract Background With limited resources available, injury prevention efforts need to be targeted both geographically and to specific populations. As part of a pediatric injury prevention project, data was obtained on all pediatric medical and injury incidents in a fire district to evaluate geographical clustering of pediatric injuries. This will be the first step in attempting to prevent these injuries with specific interventions depending on locations and mechanisms. Results There were a total of 4803 incidents involving patients less than 15 years of age that the fire district responded to during 2001–2005 of which 1997 were categorized as injuries and 2806 as medical calls. The two cohorts (injured versus medical differed in age distribution (7.7 ± 4.4 years versus 5.4 ± 4.8 years, p Conclusion Significant clustering occurs overall for all injury mechanisms combined and for each mechanism depending on the cluster detection method used. There was some overlap in geographic clusters identified by both methods. The Bernoulli method allows more focused cluster mapping and evaluation since it directly uses location data. Once clusters are found, interventions can be targeted to specific geographic locations, location types, ages of victims, and mechanisms of injury.

  8. Critérios de formação de carteiras de ativos por meio de Hierarchical Clusters

    Directory of Open Access Journals (Sweden)

    Pierre Lucena

    2010-04-01

    Full Text Available Este artigo tem como objetivo principal apresentar e testar uma ferramenta de estatística multivariada em modelos financeiros. Essa metodologia, conhecida como análise de clusters, separa as observações em grupos com suas determinadas características, em contraste com a metodologia tradicional, que é somente a ordem com os quantis. Foi aplicada essa ferramenta em 213 ações negociadas na Bolsa de São Paulo (Bovespa, separando os grupos por tamanho e book-tomarket. Depois, as novas carteiras foram aplicadas no modelo de Fama e French (1996, comparando os resultados numa formação de carteira para quantil e análise de cluster. Foram encontrados melhores resultados na segunda metodologia. Os autores concluem que a análise de cluster pode ser mais adequada porque tende a formar grupos mais homogeneizados, sendo sua aplicação útil para a formação de carteiras e para a teoria financeira.

  9. Hierarchical Fragmentation and Jet-like Outflows in IRDC G28.34+0.06, a Growing Massive Protostar Cluster

    CERN Document Server

    Wang, Ke; Wu, Yuefang; Zhang, Huawei

    2011-01-01

    We present Submillimeter Array (SMA) \\lambda = 0.88mm observations of an infrared dark cloud (IRDC) G28.34+0.06. Located in the quiescent southern part of the G28.34 cloud, the region of interest is a massive ($>10^3$\\,\\msun) molecular clump P1 with a luminosity of $\\sim 10^3$ \\lsun, where our previous SMA observations at 1.3mm have revealed a string of five dust cores of 22-64 \\msun\\ along the 1 pc IR-dark filament. The cores are well aligned at a position angle of 48 degrees and regularly spaced at an average projected separation of 0.16 pc. The new high-resolution, high-sensitivity 0.88\\,mm image further resolves the five cores into ten compact condensations of 1.4-10.6 \\msun, with sizes a few thousands AU. The spatial structure at clump ($\\sim 1$ pc) and core ($\\sim 0.1$ pc) scales indicates a hierarchical fragmentation. While the clump fragmentation is consistent with a cylindrical collapse, the observed fragment masses are much larger than the expected thermal Jeans masses. All the cores are driving CO(...

  10. 一种分层分簇的组密钥管理方案%A HIERARCHICAL CLUSTERING-BASED GROUP KEY MANAGEMENT SCHEME

    Institute of Scientific and Technical Information of China (English)

    李珍格; 游林

    2014-01-01

    为了满足无线传感器网络组通信的安全,提出一种分层分簇的组密钥管理方案。该方案采用分层的体系结构,将组中节点分为管理层和普通层。BS通过构造特殊的组密钥多项式更新普通层组密钥,而管理层则采用二元单向函数进行组密钥的协商。分析表明,该方案很好满足了无线传感器网络中组密钥管理的前向安全性,后向安全性,并且减小了存储开销、计算开销和通信开销。%In this paper,a hierarchical clustering-based group key management scheme is proposed in order to satisfy the secure group communication in wireless sensor network.The proposed scheme adopts the hierarchical architecture and divides the nodes in the group into master-node layer and terminal layer.The group key of terminal layer is updated by constructing a special group key polynomial in BS,and the binary one-way function is used by the master-node layer for group key negotiation.Analysis demonstrates that the scheme well satisfies the forward security and backward security of the group key management in WSN,and reduces the storage overhead,computation overhead and communication overhead as well.

  11. Neural Mechanisms of Hierarchical Planning in a Virtual Subway Network.

    Science.gov (United States)

    Balaguer, Jan; Spiers, Hugo; Hassabis, Demis; Summerfield, Christopher

    2016-05-18

    Planning allows actions to be structured in pursuit of a future goal. However, in natural environments, planning over multiple possible future states incurs prohibitive computational costs. To represent plans efficiently, states can be clustered hierarchically into "contexts". For example, representing a journey through a subway network as a succession of individual states (stations) is more costly than encoding a sequence of contexts (lines) and context switches (line changes). Here, using functional brain imaging, we asked humans to perform a planning task in a virtual subway network. Behavioral analyses revealed that humans executed a hierarchically organized plan. Brain activity in the dorsomedial prefrontal cortex and premotor cortex scaled with the cost of hierarchical plan representation and unique neural signals in these regions signaled contexts and context switches. These results suggest that humans represent hierarchical plans using a network of caudal prefrontal structures. VIDEO ABSTRACT.

  12. Micromechanics of hierarchical materials

    DEFF Research Database (Denmark)

    Mishnaevsky, Leon, Jr.

    2012-01-01

    A short overview of micromechanical models of hierarchical materials (hybrid composites, biomaterials, fractal materials, etc.) is given. Several examples of the modeling of strength and damage in hierarchical materials are summarized, among them, 3D FE model of hybrid composites...... with nanoengineered matrix, fiber bundle model of UD composites with hierarchically clustered fibers and 3D multilevel model of wood considered as a gradient, cellular material with layered composite cell walls. The main areas of research in micromechanics of hierarchical materials are identified, among them......, the investigations of the effects of load redistribution between reinforcing elements at different scale levels, of the possibilities to control different material properties and to ensure synergy of strengthening effects at different scale levels and using the nanoreinforcement effects. The main future directions...

  13. Analyses of crime patterns in NIBRS data based on a novel graph theory clustering method: Virginia as a case study.

    Science.gov (United States)

    Zhao, Peixin; Darrah, Marjorie; Nolan, Jim; Zhang, Cun-Quan

    2014-01-01

    This paper suggests a novel clustering method for analyzing the National Incident-Based Reporting System (NIBRS) data, which include the determination of correlation of different crime types, the development of a likelihood index for crimes to occur in a jurisdiction, and the clustering of jurisdictions based on crime type. The method was tested by using the 2005 assault data from 121 jurisdictions in Virginia as a test case. The analyses of these data show that some different crime types are correlated and some different crime parameters are correlated with different crime types. The analyses also show that certain jurisdictions within Virginia share certain crime patterns. This information assists with constructing a pattern for a specific crime type and can be used to determine whether a jurisdiction may be more likely to see this type of crime occur in their area.

  14. Analyses of Crime Patterns in NIBRS Data Based on a Novel Graph Theory Clustering Method: Virginia as a Case Study

    Directory of Open Access Journals (Sweden)

    Peixin Zhao

    2014-01-01

    Full Text Available This paper suggests a novel clustering method for analyzing the National Incident-Based Reporting System (NIBRS data, which include the determination of correlation of different crime types, the development of a likelihood index for crimes to occur in a jurisdiction, and the clustering of jurisdictions based on crime type. The method was tested by using the 2005 assault data from 121 jurisdictions in Virginia as a test case. The analyses of these data show that some different crime types are correlated and some different crime parameters are correlated with different crime types. The analyses also show that certain jurisdictions within Virginia share certain crime patterns. This information assists with constructing a pattern for a specific crime type and can be used to determine whether a jurisdiction may be more likely to see this type of crime occur in their area.

  15. Hierarchical Cluster-based Partial Least Squares Regression (HC-PLSR is an efficient tool for metamodelling of nonlinear dynamic models

    Directory of Open Access Journals (Sweden)

    Omholt Stig W

    2011-06-01

    Full Text Available Abstract Background Deterministic dynamic models of complex biological systems contain a large number of parameters and state variables, related through nonlinear differential equations with various types of feedback. A metamodel of such a dynamic model is a statistical approximation model that maps variation in parameters and initial conditions (inputs to variation in features of the trajectories of the state variables (outputs throughout the entire biologically relevant input space. A sufficiently accurate mapping can be exploited both instrumentally and epistemically. Multivariate regression methodology is a commonly used approach for emulating dynamic models. However, when the input-output relations are highly nonlinear or non-monotone, a standard linear regression approach is prone to give suboptimal results. We therefore hypothesised that a more accurate mapping can be obtained by locally linear or locally polynomial regression. We present here a new method for local regression modelling, Hierarchical Cluster-based PLS regression (HC-PLSR, where fuzzy C-means clustering is used to separate the data set into parts according to the structure of the response surface. We compare the metamodelling performance of HC-PLSR with polynomial partial least squares regression (PLSR and ordinary least squares (OLS regression on various systems: six different gene regulatory network models with various types of feedback, a deterministic mathematical model of the mammalian circadian clock and a model of the mouse ventricular myocyte function. Results Our results indicate that multivariate regression is well suited for emulating dynamic models in systems biology. The hierarchical approach turned out to be superior to both polynomial PLSR and OLS regression in all three test cases. The advantage, in terms of explained variance and prediction accuracy, was largest in systems with highly nonlinear functional relationships and in systems with positive feedback

  16. Segmentation Algorithm for Oil Spill SAR Images Based on Hierarchical Agglomerative Clustering%基于HAC的溢油SAR图像分割算法

    Institute of Scientific and Technical Information of China (English)

    苏腾飞; 孟俊敏; 张晰

    2013-01-01

    图像分割是SAR溢油检测中的关键步骤,但由于SAR影像中存在斑点噪声,使得一般的图像分割算法难以收到理想的效果,严重影响溢油检测的精度.发展一种基于凝聚层次聚类(Hierarchical Agglomerative Clustering,HAC)的溢油SAR图像分割算法.该算法利用多尺度分割的思想,能够有效保持SAR影像中溢油斑块的形状特征,并能减少细碎斑块的产生.利用2010年墨西哥湾的Envisat ASAR影像开展了溢油SAR图像分割实验,并将该算法和Canny边缘检测、OTSU阈值分割、FCM分割、水平集分割等方法进行了对比.结果显示,HAC方法可以有效减少细碎斑块的产生,有助于提高SAR溢油检测的精度.%Image segmentation is a crucial stage in the SAR oil spill detection.However,the common image segmentation algorithms can hardly achieve satisfactory results due to speckle noise in the SAR images,thus affecting seriously the accuracy of oil spill detection.For this reason,an image segmentation algorithm which is based on HAC (Hierarchical Agglomerative Clustering) is developed for the oil spill SAR images.This method takes advantage of multi-resolution segmentation to maintain effectively the shape property of oil spill patches,and can reduce the formation of small patches.By using Envisat ASAR images of the Gulf of Mexico obtained in 2010,an experiment of SAR oil spill image segmentation has been conducted.Comparing with other approaches such as Canny,OTSU,FCM and Levelset,the results show that HAC can effectively reduce the producing of small patches,which is helpful to improve the accuracy of SAR oil spill detection.

  17. Engineering, Trade, and Technical Cluster. Task Analyses. Drafting and Design Technology, Precision Machining Technology, Electronics Technology.

    Science.gov (United States)

    Henrico County Public Schools, Glen Allen, VA. Virginia Vocational Curriculum and Resource Center.

    Developed in Virginia, this publication contains task analysis guides to support selected tech prep programs that prepare students for careers in the engineering, trade, and technical cluster. Three occupations are profiled: drafting and design technology, precision machining technology, and electronics technology. Each guide contains the…

  18. Whisper, a resonance sounder and wave analyser: Performances and perspectives for the Cluster mission

    DEFF Research Database (Denmark)

    Decreau, P.M.E.; Fergeau, P.; KrannoselsKikh, V.

    1997-01-01

    The WHISPER sounder on the Cluster spacecraft is primarily designed to provide an absolute measurement of the total plasma density within the range 0.2-80 cm(-3). This is achieved by means of a resonance sounding technique which has already proved successful in the regions to be explored. The wav...

  19. Cluster and factor analyses using water quality data in the Sapkyo reservoir watershed

    Energy Technology Data Exchange (ETDEWEB)

    Rim, Chang-Soo [Chungwoon University, Hongsung(Korea); Shin, Jae-Ki [Inje University, Kimhae(Korea)

    2002-04-30

    The monthly water quality data measured at 19 stations located in the Sapkyo reservoir watershed were clustered into 2 to 7 clusters and factor analysis was conducted to characterize the water quality, using the information obtained from cluster analysis. The result of cluster analysis shows that Sapkyo reservoir and each stream (Sapkyo stream, Muhan stream and Kokkyo stream) in Sapkyo reservoir watershed have their own water quality characteristics. The result of water quality analysis indicates that the concentration of suspended solids from Sapkyo reservoir is much higher than those of other streams, and which is probably because of increment of phytoplankton biomass with rich nutrient flowing into Sapkyo reservoir from the upper stream of watershed. Furthermore, the concentrations of biochemical oxygen demand and chemical oxygen demand were 3.5 to 4.8 times and 1.7 to 2.5 times those of other streams, respectively. The overall water quality of Sapkyo reservoir watershed was considered to exceed eutrophic condition. Based on factor analysis, the water quality characteristics of Sapkyo stream and Muhan stream were closely related with farm land and residence. The water quality of Kokkyo stream was influenced by superabundant organic matter flowing from Chonan city and district wastewater treatment plant located in the upper stream of Kokkyo stream. The water quality factor influencing Sapkyo reservoir was closely related with water quality factors of other three streams. (author). 20 refs., 6 tabs., 3 figs.

  20. Group analyses of connectivity-based cortical parcellation using repeated k-means clustering

    NARCIS (Netherlands)

    Nanetti, Luca; Cerliani, Leonardo; Gazzola, Valeria; Renken, Remco; Keysers, Christian

    2009-01-01

    K-means clustering has become a popular tool for connectivity-based cortical segmentation using Diffusion Weighted Imaging (DWI) data. A sometimes ignored issue is, however, that the output of the algorithm depends on the initial placement of starting points, and that different sets of starting poin

  1. Whisper, a resonance sounder and wave analyser: Performances and perspectives for the Cluster mission

    DEFF Research Database (Denmark)

    Decreau, P.M.E.; Fergeau, P.; KrannoselsKikh, V.;

    1997-01-01

    The WHISPER sounder on the Cluster spacecraft is primarily designed to provide an absolute measurement of the total plasma density within the range 0.2-80 cm(-3). This is achieved by means of a resonance sounding technique which has already proved successful in the regions to be explored. The wav...

  2. Partial least square and hierarchical clustering in ADMET modeling: prediction of blood-brain barrier permeation of α-adrenergic and imidazoline receptor ligands.

    Science.gov (United States)

    Nikolic, Katarina; Filipic, Slavica; Smoliński, Adam; Kaliszan, Roman; Agbaba, Danica

    2013-01-01

    PURPOSE. Rate of brain penetration (logPS), brain/plasma equilibration rate (logPS-brain), and extent of blood-brain barrier permeation (logBB) of 29 α-adrenergic and imidazoline-receptors ligands were examined in Quantitative-Structure-Property Relationship (QSPR) study. METHODS. Experimentally determined chromatographic retention data (logKw at pH 4.4, slope (S) at pH 4.4, logKw at pH 7.4, slope (S) at pH 7.4, logKw at pH 9.1, and slope (S) at pH 9.1) and capillary electrophoresis migration parameters (μeff at pH 4.4, μeff at pH 7.4, and μeff at pH 9.1), together with calculated molecular descriptors, were used as independent variables in the QSPR study by use of partial least square (PLS) methodology. RESULTS. Predictive potential of the formed QSPR models, QSPR(logPS), QSPR(logPS-brain), QSPR(logBB), was confirmed by cross- and external validation. Hydrophilicity (Hy) and H-indices (H7m) were selected as significant parameters negatively correlated with both logPS and logPS-brain, while topological polar surface area (TPSA(NO)) was chosen as molecular descriptor negatively correlated with both logPS and logBB. The principal component analysis (PCA) and hierarchical clustering analysis (HCA) were applied to cluster examined drugs based on their chromatographic, electrophoretic and molecular properties. Significant positive correlations were obtained between the slope (S) at pH 7.4 and logBB in A/B cluster and between the logKw at pH 9.1 and logPS in C/D cluster. CONCLUSIONS. Results of the QSPR, clustering and correlation studies could be used as novel tool for evaluation of blood-brain barrier permeation of related α-adrenergic/imidazoline receptor ligands.This article is open to POST-PUBLICATION REVIEW. Registered readers (see "For Readers") may comment by clicking on ABSTRACT on the issue's contents page.PURPOSE. Rate of brain penetration (logPS), brain/plasma equilibration rate (logPS-brain), and extent of blood-brain barrier permeation (logBB) of 29

  3. Creative clusters and place-making: analysing the quality of place in Soho and Beyoglu

    OpenAIRE

    2012-01-01

    During the last decade creativity has become one of the buzz concepts of urban practice and research, and new concepts such as the creative city, creative economy, the creative class, creative industries and creative clusters have emerged (Florida, 2002; Landry, 2000). There are studies in economics and cultural geography, sociology and, to some extent in urban planning, exploring the creative city phenomenon. To date, however, there have only been a limited number of studies on understanding...

  4. Cluster Ion Spectrometry (CIS) data quality indexes as a support for analysing magnetospheric measurements

    Science.gov (United States)

    Dandouras, Iannis; Barthe, Alain; Brunato, Sylvain; Rème, Henri; Laakso, Harri

    2016-04-01

    The Cluster Science Archive (CSA) aims at preserving the complete set of the measurements collected by the four Cluster spacecraft, so that they are usable in the long-term by the world-wide scientific community as well as by the instrument teams. This implies that the instrument data, properly calibrated, are filed together with the descriptive and documentary elements making it possible to select and interpret them. The CIS (Cluster Ion Spectrometry) experiment is a comprehensive ionic plasma spectrometry package onboard the Cluster spacecraft, capable of obtaining full three-dimensional ion distributions (about 0 to 40 keV/e) with a time resolution of one spacecraft spin (4 sec) and with mass-per-charge composition determination. For the archival of the CIS data a multi-level approach has been adopted. The CSA archival includes processed raw data, moments of the ion distribution functions, and calibrated high-resolution data in a variety of physical units. The latter are 3-D ion distribution functions, 2-D pitch-angle distributions and 1-D omni-directional fluxes. The CIS data archive includes also experiment documentation, graphical products for browsing through the data, data caveats and data quality indexes. The later constitute a novel product, which has been prepared in order to help the user asses the quality of the data acquired in different magnetospheric regions and during various operational modes. It provides information on which are in each case the issues that can affect the data quality, which are the data products affected, and gives a simple quantitative measurement of the severity of these issues. The principle of the CIS data quality indexes will be described and the various issues, that can under some conditions affect the data quality and are thus taken into account in generating the data quality indexes, will be discussed.

  5. Identification and comparative analyses of Siamois cluster genes in Xenopus laevis and tropicalis.

    Science.gov (United States)

    Haramoto, Yoshikazu; Saijyo, Tomohito; Tanaka, Toshiaki; Furuno, Nobuaki; Suzuki, Atsushi; Ito, Yuzuru; Kondo, Mariko; Taira, Masanori; Takahashi, Shuji

    2017-06-15

    Two siamois-related homeobox genes siamois (sia1) and twin (sia2), have been reported in Xenopus laevis. These genes are expressed in the blastula chordin- and noggin-expressing (BCNE) center and the Nieuwkoop center, and have complete secondary axis-inducing activity when over-expressed on the ventral side of the embryo. Using whole genome sequences of X. tropicalis and X. laevis, we identified two additional siamois-related genes, which are tandemly duplicated near sia1 and sia2 to form the siamois gene cluster. Four siamois genes in X. tropicalis are transcribed at blastula to gastrula stages. In X. laevis, the siamois gene cluster is present on both homeologous chromosomes, XLA3L and XLA3S. Transcripts from seven siamois genes (three on XLA3L and four on XLA3S) in X. laevis were detected at blastula to gastrula stages. A transcribed gene, sia1p. S, encodes an inactive protein without a homeodomain. When over-expressed ventrally, all siamois-related genes tested in this study except for sia1p. S induced a complete secondary axis, indicating that X. tropicalis and X. laevis have four and six active siamois-related genes, respectively. Of note, each gene required different amounts of mRNA for full activity. These results suggest the possibility that siamois cluster genes have functional redundancy to endow robustness and quickness to organizer formation in Xenopus species. Copyright © 2017. Published by Elsevier Inc.

  6. Ultra high performance liquid chromatography with electrospray ionization tandem mass spectrometry coupled with hierarchical cluster analysis to evaluate Wikstroemia indica (L.) C. A. Mey. from different geographical regions.

    Science.gov (United States)

    Wei, Lan; Wang, Xiaobo; Mu, Shanxue; Sun, Lixin; Yu, Zhiguo

    2015-06-01

    A sensitive, rapid and simple ultra high performance liquid chromatography with electrospray ionization tandem mass spectrometry method was developed to determine seven constituents (umbelliferone, apigenin, triumbelletin, daphnoretin, arctigenin, genkwanin and emodin) in Wikstroemia indica (L.) C. A. Mey. The chromatographic analysis was performed on an ACQUITY UPLC® BEH C18 column (2.1 × 50 mm, 1.7 μm) by gradient elution with the mobile phase of 0.05% formic acid aqueous solution (A) and acetonitrile (B). Multiple reaction monitoring mode with positive and negative electrospray ionization interface was carried out to detect the components. This method was validated in terms of specificity, linearity, accuracy, precision and stability. Excellent linear behavior was observed over the certain concentration ranges with the correlation coefficient values higher than 0.999. The intraday and innerday precisions were within 2.0%. The recoveries of seven analytes were 99.4-101.1% with relative standard deviation less than 1.2%. The 18 Wikstroemia indica samples from different origins were classified by hierarchical clustering analysis according to the contents of seven components. The results demonstrated that the developed method could successfully be used to quantify simultaneously of seven components in Wikstroemia indica and could be a helpful tool for the detection and confirmation of the quality of traditional Chinese medicines.

  7. Quantitative and Chemical Fingerprint Analysis for the Quality Evaluation of Receptaculum Nelumbinis by RP-HPLC Coupled with Hierarchical Clustering Analysis

    Directory of Open Access Journals (Sweden)

    Jin-Zhong Wu

    2013-01-01

    Full Text Available A simple and reliable method of high-performance liquid chromatography with photodiode array detection (HPLC-DAD was developed to evaluate the quality of Receptaculum Nelumbinis (dried receptacle of Nelumbo nucifera through establishing chromatographic fingerprint and simultaneous determination of five flavonol glycosides, including hyperoside, isoquercitrin, quercetin-3-O-β-d-glucuronide, isorhamnetin-3-O-β-d-galactoside and syringetin-3-O-β-d-glucoside. In quantitative analysis, the five components showed good regression (R > 0.9998 within linear ranges, and their recoveries were in the range of 98.31%–100.32%. In the chromatographic fingerprint, twelve peaks were selected as the characteristic peaks to assess the similarities of different samples collected from different origins in China according to the State Food and Drug Administration (SFDA requirements. Furthermore, hierarchical cluster analysis (HCA was also applied to evaluate the variation of chemical components among different sources of Receptaculum Nelumbinis in China. This study indicated that the combination of quantitative and chromatographic fingerprint analysis can be readily utilized as a quality control method for Receptaculum Nelumbinis and its related traditional Chinese medicinal preparations.

  8. HILIC-UPLC-MS/MS combined with hierarchical clustering analysis to rapidly analyze and evaluate nucleobases and nucleosides in Ginkgo biloba leaves.

    Science.gov (United States)

    Yao, Xin; Zhou, Guisheng; Tang, Yuping; Guo, Sheng; Qian, Dawei; Duan, Jin-Ao

    2015-02-01

    Ginkgo biloba leaf extract has been widely used in dietary supplements and more recently in some foods and beverages. In addition to the well-known flavonol glycosides and terpene lactones, G. biloba leaves are also rich in nucleobases and nucleosides. To determine the content of nucleobases and nucleosides in G. biloba leaves at trace levels, a reliable method has been established by using hydrophilic interaction ultra performance liquid chromatography coupled with triple-quadrupole tandem mass spectrometry (HILIC-UPLC-TQ-MS/MS) working in multiple reaction monitoring mode. Eleven nucleobases and nucleosides were simultaneously determined in seven min. The proposed method was fully validated in terms of linearity, sensitivity, and repeatability, as well as recovery. Furthermore, hierarchical clustering analysis (HCA) was performed to evaluate and classify the samples according to the contents of the eleven chemical constituents. The established approach could be helpful for evaluation of the potential values as dietary supplements and the quality control of G. biloba leaves, which might also be utilized for the investigation of other medicinal herbs containing nucleobases and nucleosides.

  9. Avoiding progenitor bias: The structural and mass evolution of Brightest Group and Cluster Galaxies in Hierarchical models since z~1

    CERN Document Server

    Shankar, Francesco; Rettura, Alessandro; Bouillot, Vincent; Moreno, Jorge; Licitra, Rossella; Bernardi, Mariangela; Huertas-Company, Marc; Mei, Simona; Ascaso, Begoña; Sheth, Ravi; Delaye, Lauriane; Raichoor, Anand

    2015-01-01

    The mass and structural evolution of massive galaxies is one of the hottest topics in galaxy formation. This is because it may reveal invaluable insights into the still debated evolutionary processes governing the growth and assembly of spheroids. However, direct comparison between models and observations is usually prevented by the so-called "progenitor bias", i.e., new galaxies entering the observational selection at later epochs, thus eluding a precise study of how pre-existing galaxies actually evolve in size. To limit this effect, we here gather data on high-redshift brightest group and cluster galaxies, evolve their (mean) host halo masses down to z=0 along their main progenitors, and assign as their "descendants" local SDSS central galaxies matched in host halo mass. At face value, the comparison between high redshift and local data suggests a noticeable increase in stellar mass of a factor of >2 since z~1, and of >2.5 in mean effective radius. We then compare the inferred stellar mass and size growth ...

  10. Divisive Analysis (DIANA of hierarchical clustering and GPS data for level of service criteria of urban streets

    Directory of Open Access Journals (Sweden)

    Ashish Kumar Patnaik

    2016-03-01

    Full Text Available Level of Service (LOS for heterogeneous traffic flow on urban streets is not well defined in Indian context. Hence in this study an attempt is taken to classify urban road networks into number of street classes and average travel speeds on street segments into LOS categories. Divisive Analysis (DIANA Clustering is used for such classification of large amount of speed data collected using GPS receiver. DIANA algorithm and silhouette validation parameter are used to classify Free Flow Speeds (FFS into optimal number of classes and the same algorithm is applied on speed data to determine ranges of different LOS categories. Speed ranges for LOS categories (A–F expressed in percentage of FFS are found to be 90, 70, 50, 40, 25 and 20–25 respectively in the present study. On the other hand, in HCM (2000 it has been mentioned these values are 85 and above, 67–85, 50–67, 40–50, 30–40 and 30 and less percent respectively.

  11. Clustering Analyses of 300,000 Photometrically Classified Quasars--I. Luminosity and Redshift Evolution in Quasar Bias

    CERN Document Server

    Myers, A D; Nichol, R C; Richards, G T; Schneider, D P; Bahcall, N A; Myers, Adam D.; Brunner, Robert J.; Nichol, Robert C.; Richards, Gordon T.; Schneider, Donald P.; Bahcall, Neta A.

    2006-01-01

    Using ~300,000 photometrically classified quasars, by far the largest quasar sample ever used for such analyses, we study the redshift and luminosity evolution of quasar clustering on scales of ~50 kpc/h to ~20 Mpc/h from redshifts of z~0.75 to z~2.28. We parameterize our clustering amplitudes using realistic dark matter models, and find that a LCDM power spectrum provides a superb fit to our data with a redshift-averaged quasar bias of b_Q = 2.41+/-0.08 ($P_{99.6% using our data set alone, increasing to >99.9999% if stellar contamination is not explicitly parameterized. We measure the quasar classification efficiency across our full sample as a = 95.6 +/- ^{4.4}_{1.9}%, a star-quasar separation comparable with the star-galaxy separation in many photometric studies of galaxy clustering. We derive the mean mass of the dark matter halos hosting quasars as MDMH=(5.2+/-0.6)x10^{12} M_solar/h. At z~1.9 we find a $1.5\\sigma$ deviation from luminosity-independent quasar clustering; this suggests that increasing our ...

  12. Cluster analysis application for analysing a diversification in a perception of cooperative banks

    Directory of Open Access Journals (Sweden)

    Anna Bieniasz

    2010-01-01

    Full Text Available The aim of this paper was to analyse a regional diversification in a perception of cooperative banks among students who come from farms in central and west Poland. The aim was realised by the comparison of the students’ in the analysed voivodships view about 13 statements concerning cooperative banks. The next step was to distinguish between voivodships 3 segments – with positive, neutral and negative outlook on the cooperative banks and to describe the distinguished segments.

  13. Weighted Clustering

    DEFF Research Database (Denmark)

    Ackerman, Margareta; Ben-David, Shai; Branzei, Simina

    2012-01-01

    We investigate a natural generalization of the classical clustering problem, considering clustering tasks in which different instances may have different weights.We conduct the first extensive theoretical analysis on the influence of weighted data on standard clustering algorithms in both...... the partitional and hierarchical settings, characterizing the conditions under which algorithms react to weights. Extending a recent framework for clustering algorithm selection, we propose intuitive properties that would allow users to choose between clustering algorithms in the weighted setting and classify...

  14. Formation of bimetallic clusters in superfluid helium nanodroplets analysed by atomic resolution electron tomography

    Science.gov (United States)

    Haberfehlner, Georg; Thaler, Philipp; Knez, Daniel; Volk, Alexander; Hofer, Ferdinand; Ernst, Wolfgang E.; Kothleitner, Gerald

    2015-10-01

    Structure, shape and composition are the basic parameters responsible for properties of nanoscale materials, distinguishing them from their bulk counterparts. To reveal these in three dimensions at the nanoscale, electron tomography is a powerful tool. Advancing electron tomography to atomic resolution in an aberration-corrected transmission electron microscope remains challenging and has been demonstrated only a few times using strong constraints or extensive filtering. Here we demonstrate atomic resolution electron tomography on silver/gold core/shell nanoclusters grown in superfluid helium nanodroplets. We reveal morphology and composition of a cluster identifying gold- and silver-rich regions in three dimensions and we estimate atomic positions without using any prior information and with minimal filtering. The ability to get full three-dimensional information down to the atomic scale allows understanding the growth and deposition process of the nanoclusters and demonstrates an approach that may be generally applicable to all types of nanoscale materials.

  15. Discovering hierarchical structure in normal relational data

    DEFF Research Database (Denmark)

    Schmidt, Mikkel Nørgaard; Herlau, Tue; Mørup, Morten

    2014-01-01

    Hierarchical clustering is a widely used tool for structuring and visualizing complex data using similarity. Traditionally, hierarchical clustering is based on local heuristics that do not explicitly provide assessment of the statistical saliency of the extracted hierarchy. We propose a non-param...

  16. Discovery of Overlapping and Hierarchical Communities Based on Extended Link Cluster Sequence%基于增广边簇序列的重叠层次社区发现

    Institute of Scientific and Technical Information of China (English)

    郭红; 黄佳鑫; 郭昆

    2015-01-01

    The mining and discovery of overlapping and hierarchical communities is a hot topic in the area of social network research. Firstly, an algorithm, discovery of link conmunities based on extended link cluster sequence ( DLC ECS) , is proposed to detect overlapping and hierarchical communities in social networks efficiently. Based on the extended link cluster sequence corresponding to community structures with various densities, the optimal link community is detected after searching for the global optimal density. The link communities are transformed into the node communities, and thus the overlapping communities can be found out. Then, hierarchical link communities extraction based on extended link cluster sequence ( HLCE ECS ) is designed. Hierarchical link communities from the extended link cluster sequence is found by the proposed algorithm. The link communities are transformed into the node communities to find out the overlapping and hierarchical communities. Experimental results on are artificial and real-world datasets demonstrate that DLC ECS algorithm significantly improves the community quality and HLCE ECS algorithm effectively discovers meaningful hierarchical communities.%高质量重叠层次社区的挖掘和发现已成为社会网络研究热点,为更有效地发现社会网络中具有重叠层次性的社区结构,提出基于增广边簇序列的边社区发现算法( DLC ECS)。在产生包含所有可能密度参数对应的社区结构的增广边簇序列的基础上,找出全局最优的密度参数,发现全局最优的边社区结构,将识别的边社区结构转化为节点社区结构,发现具有重叠结构的社区。在该序列的基础上,提出层次边社区提取算法( HLCE ECS),快速发现序列中的层次边社区结构,将识别的边社区结构转化为节点社区结构,发现同时具有重叠和层次结构的社区。在真实数据集和人工数据集上的实验表明,DLC ECS具有

  17. Cluster analyses of 20th century growth patterns in high elevation Great Basin bristlecone pine in the Snake Mountain Range, Nevada, USA

    Science.gov (United States)

    Tran, T. J.; Bruening, J. M.; Bunn, A. G.; Salzer, M. W.; Weiss, S. B.

    2015-12-01

    Great Basin bristlecone pine (Pinus longaeva) is a useful climate proxy because of the species' long lifespan (up to 5000 years) and the climatic sensitivity of its annually-resolved rings. Past studies have shown that growth of individual trees can be limited by temperature, soil moisture, or a combination of the two depending on biophysical setting at the scale of tens of meters. We extend recent research suggesting that trees vary in their growth response depending on their position on the landscape to analyze how growth patterns vary over time. We used hierarchical cluster analysis to examine the growth of 52 bristlecone pine trees near the treeline of Mount Washington, Nevada, USA. We classified growth of individual trees over the instrumental climate record into one of two possible scenarios: trees belonging to a temperature-sensitive cluster and trees belonging to a precipitation-sensitive cluster. The number of trees in the precipitation-sensitive cluster outnumbered the number of trees in the temperature-sensitive cluster, with trees in colder locations belonging to the temperature-sensitive cluster. When we separated the temporal range into two sections (1895-1949 and 1950-2002) spanning the length of the instrumental climate record, we found that most of the 52 trees remained loyal to their cluster membership (e.g., trees in the temperature-sensitive cluster in 1895-1949 were also in the temperature sensitive cluster in 1950-2002), though not without exception. Of those trees that do not remain consistent in cluster membership, the majority changed from temperature-sensitive to precipitation-sensitive as time progressed. This could signal a switch from temperature limitation to water limitation with warming climate. We speculate that topographic complexity in high mountain environments like Mount Washington might allow for climate refugia where growth response could remain constant over the Holocene.

  18. Hierarchical clustering of genetic diversity associated to different levels of mutation and recombination in Escherichia coli: a study based on Mexican isolates.

    Science.gov (United States)

    González-González, Andrea; Sánchez-Reyes, Luna L; Delgado Sapien, Gabriela; Eguiarte, Luis E; Souza, Valeria

    2013-01-01

    Escherichia coli occur as either free-living microorganisms, or within the colons of mammals and birds as pathogenic or commensal bacteria. Although the Mexican population of intestinal E. coli maintains high levels of genetic diversity, the exact mechanisms by which this occurs remain unknown. We therefore investigated the role of homologous recombination and point mutation in the genetic diversification and population structure of Mexican strains of E. coli. This was explored using a multi locus sequence typing (MLST) approach in a non-outbreak related, host-wide sample of 128 isolates. Overall, genetic diversification in this sample appears to be driven primarily by homologous recombination, and to a lesser extent, by point mutation. Since genetic diversity is hierarchically organized according to the MLST genealogy, we observed that there is not a homogeneous recombination rate, but that different rates emerge at different clustering levels such as phylogenetic group, lineage and clonal complex (CC). Moreover, we detected clear signature of substructure among the A+B1 phylogenetic group, where the majority of isolates were differentiated into four discrete lineages. Substructure pattern is revealed by the presence of several CCs associated to a particular life style and host as well as to different genetic diversification mechanisms. We propose these findings as an alternative explanation for the maintenance of the clear phylogenetic signal of this species despite the prevalence of homologous recombination. Finally, we corroborate using both phylogenetic and genetic population approaches as an effective mean to establish epidemiological surveillance tailored to the ecological specificities of each geographic region.

  19. Interpolation centers' selection using hierarchical curvature-based clustering Selección de centros de interpolacion mediante agrupamiento jerárquico basado en curvatura

    Directory of Open Access Journals (Sweden)

    Juan C. Rodríguez

    2010-07-01

    Full Text Available Es ampliamente conocido que algunos campos relacionados con aplicaciones de gráficos realistas requieren modelos tridimensionales altamente detallados. Las tecnologías para esto están bien desarrolladas, sin embargo, en algunos casos los escáneres láser obtienen modelos complejos formados por millones de puntos, por lo que son computacionalmente intratables. En estos casos es conveniente obtener un conjunto reducido de estas muestras con las que reconstruir la superficie de la función. Obtener un enfoque de reducción adecuado que posea un equilibrio entre la pérdida de precisión de la función reconstruida, y el costo computacional es un problema no trivial. En este artículo presentamos un método jerárquico de aglomeración a través de la selección de centros mediante la geométrica, la distribución y la estimación de curvatura de las muestras en el espacio 3D.It is widely known that some fields related to graphic applications require realistic and full detailed three-dimensional models. Technologies for this kind of applications exist. However, in some cases, laser scanner get complex models composed of million of points, making its computationally difficult. In these cases, it is desirable to obtain a reduced set of these samples to reconstruct the function's surface. An appropriate reduction approach with a non-significant loss of accuracy in the reconstructed function with a good balance of computational load is usually a non-trivial problem. In this article, a hierarchical clustering based method by the selection of center using the geometric distribution and curvature estimation of the samples in the 3D space is described.

  20. Analysing the spatial patterns of livestock anthrax in Kazakhstan in relation to environmental factors: a comparison of local (Gi* and morphology cluster statistics

    Directory of Open Access Journals (Sweden)

    Ian T. Kracalik

    2012-11-01

    Full Text Available We compared a local clustering and a cluster morphology statistic using anthrax outbreaks in large (cattle and small (sheep and goats domestic ruminants across Kazakhstan. The Getis-Ord (Gi* statistic and a multidirectional optimal ecotope algorithm (AMOEBA were compared using 1st, 2nd and 3rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and Gi* tests. A logistic regression was used to define a risk surface for anthrax outbreaks and to compare agreement between clustering methodologies. Tests revealed differences in the spatial distribution of clusters as well as the total number of clusters in large ruminants for AMOEBA (n = 149 and for small ruminants (n = 9. In contrast, Gi* revealed fewer large ruminant clusters (n = 122 and more small ruminant clusters (n = 61. Significant environmental differences were found between groups using the Kruskall-Wallis and Mann- Whitney U tests. Logistic regression was used to model the presence/absence of anthrax outbreaks and define a risk surface for large ruminants to compare with cluster analyses. The model predicted 32.2% of the landscape as high risk. Approximately 75% of AMOEBA clusters corresponded to predicted high risk, compared with ~64% of Gi* clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while Gi* tended to predict larger, circular clusters. Here we provide an evaluation of both tests and a discussion of the use of each to detect environmental conditions associated with anthrax outbreak clusters in domestic livestock. These findings illustrate important differences in spatial statistical methods for defining local clusters and highlight the importance of selecting appropriate levels of data aggregation.

  1. Analysing the spatial patterns of livestock anthrax in Kazakhstan in relation to environmental factors: a comparison of local (Gi*) and morphology cluster statistics.

    Science.gov (United States)

    Kracalik, Ian T; Blackburn, Jason K; Lukhnova, Larisa; Pazilov, Yerlan; Hugh-Jones, Martin E; Aikimbayev, Alim

    2012-11-01

    We compared a local clustering and a cluster morphology statistic using anthrax outbreaks in large (cattle) and small (sheep and goats) domestic ruminants across Kazakhstan. The Getis-Ord (Gi*) statistic and a multidirectional optimal ecotope algorithm (AMOEBA) were compared using 1st, 2nd and 3rd order Rook contiguity matrices. Multivariate statistical tests were used to evaluate the environmental signatures between clusters and non-clusters from the AMOEBA and Gi* tests. A logistic regression was used to define a risk surface for anthrax outbreaks and to compare agreement between clustering methodologies. Tests revealed differences in the spatial distribution of clusters as well as the total number of clusters in large ruminants for AMOEBA (n = 149) and for small ruminants (n = 9). In contrast, Gi* revealed fewer large ruminant clusters (n = 122) and more small ruminant clusters (n = 61). Significant environmental differences were found between groups using the Kruskall-Wallis and Mann-Whitney U tests. Logistic regression was used to model the presence/absence of anthrax outbreaks and define a risk surface for large ruminants to compare with cluster analyses. The model predicted 32.2% of the landscape as high risk. Approximately 75% of AMOEBA clusters corresponded to predicted high risk, compared with ~64% of Gi* clusters. In general, AMOEBA predicted more irregularly shaped clusters of outbreaks in both livestock groups, while Gi* tended to predict larger, circular clusters. Here we provide an evaluation of both tests and a discussion of the use of each to detect environmental conditions associated with anthrax outbreak clusters in domestic livestock. These findings illustrate important differences in spatial statistical methods for defining local clusters and highlight the importance of selecting appropriate levels of data aggregation.

  2. Analysing a large dataset on long-term monitoring of water quality and plankton with the SOM clustering

    Directory of Open Access Journals (Sweden)

    Voutilainen A.

    2012-11-01

    Full Text Available The Self-Organizing Map (SOM proved to be the method of choice for analysing a large heterogeneous ecological dataset. In addition to distributing the data into clusters, the SOM enabled hunting for correlations between the data components. This revealed logical and plausible relationships between and within the environment and groups of organisms. The main conclusions derived from the results were: (i the structure of early summer plankton community significantly differed from that of late summer community in Lake Pyhäselkä and (ii plankton community in late summer was characterized by two functional groups. The first group was formed mainly by phytoplankton, rotifers, and small cladocerans, such as Bosmina spp., and driven by water temperature. The second group was formed by small copepods and the abundant generalist herbivorous cladocerans Daphnia cristata and Limnosida frontosa, which, in turn, associated with chlorophyll a concentration. Biomasses of Bosmina spp. and D. cristata showed decreasing monotonic trends during a 20-year study period supposedly due to oligotrophication. Versatile possibilities to cluster data and hunt for correlations between data components offered by the SOM decisively helped to reveal associations across the original variables and draw conclusions. The results would have been undetectable solely on the basis of unorganised values.

  3. Time clustered sampling can inflate the inferred substitution rate in foot-and-mouth disease virus analyses

    DEFF Research Database (Denmark)

    Pedersen, Casper-Emil Tingskov; Frandsen, Peter; Wekesa, Sabenzia N.;

    2015-01-01

    With the emergence of analytical software for the inference of viral evolution, a number of studies have focused on estimating important parameters such as the substitution rate and the time to the most recent common ancestor (tMRCA) for rapidly evolving viruses. Coupled with an increasing...... through a study of the foot-and-mouth (FMD) disease virus serotypes SAT 1 and SAT 2. Our study shows that clustered temporal sampling in phylogenetic analyses of FMD viruses will strongly bias the inferences of substitution rates and tMRCA because the inferred rates in such data sets reflect a rate closer...... to the mutation rate rather than the substitution rate. Estimating evolutionary parameters from viral sequences should be performed with due consideration of the differences in short-term and longer-term evolutionary processes occurring within sets of temporally sampled viruses, and studies should carefully...

  4. 基于多空间多层次谱聚类的非监督SAR图像分割算法%Segmentation method for SAR images based on unsupervised spectral clustering of multi-hierarchical region

    Institute of Scientific and Technical Information of China (English)

    田玲; 邓旌波; 廖紫纤; 石博; 何楚

    2013-01-01

    提出了一种基于多层区域谱聚类的非监督SAR图像分割算法(multi-space and multi-hierarchical region based spectral clustering,MSMHSC).该算法首先在特征与几何空间求距离,快速获得初始过分割区域,然后在过分割区域的谱空间上进行聚类,最终实现非监督的SAR图像分割.该方法计算复杂度小,无须训练样本,使用层次化思想使其能更充分地利用SAR图像各类先验与似然信息.在MSTAR真实SAR数据集上的实验验证了该算法的快速性和有效性.%This paper proposed a method based on the hierarchical clustering concept.First,it over-segmented the source image into many small regions.And then,it conducted a spectral clustering algorithm on those regions.The algorithm was tested on the MSTAR SAR data set,and was proved to be fast and efficient.

  5. Research of Parallel Programming Techniques of Hierarchical Model Based on SMP Clusters%基于SMP机群的层次化并行编程技术的研究

    Institute of Scientific and Technical Information of China (English)

    祝永志; 张丹丹; 曹宝香; 禹继国

    2012-01-01

    针对多核SMP机群的体系结构特点,讨论了MPI+ OpenMP混合并行程序设计技术.提出了一种多层次化混合设计新方法.设计了N-body问题的多层次化并行算法,并在曙光5000A机群上与传统的混合算法作了性能方面的比较.结果表明,该层次化混合并行算法具有更好的扩展性和加速比.%For multi-core SMP cluster systems, this paper discusses hybrid parallel programming techniques based on MPI and OpenMP.We propose a new hybrid parallel programming methods lhat are aware of architecture hierarchy on SMP cluster systems. We design a hierarchically parallel algorithm on the N-body problem, and compared its performance with traditional hybrid parallel algorithms on the Dawning 5000A cluster. The results indicate that our hierarchically hybrid parallel algorithm has better scalability and speedup than others.

  6. Hierarchical photocatalysts.

    Science.gov (United States)

    Li, Xin; Yu, Jiaguo; Jaroniec, Mietek

    2016-05-01

    As a green and sustainable technology, semiconductor-based heterogeneous photocatalysis has received much attention in the last few decades because it has potential to solve both energy and environmental problems. To achieve efficient photocatalysts, various hierarchical semiconductors have been designed and fabricated at the micro/nanometer scale in recent years. This review presents a critical appraisal of fabrication methods, growth mechanisms and applications of advanced hierarchical photocatalysts. Especially, the different synthesis strategies such as two-step templating, in situ template-sacrificial dissolution, self-templating method, in situ template-free assembly, chemically induced self-transformation and post-synthesis treatment are highlighted. Finally, some important applications including photocatalytic degradation of pollutants, photocatalytic H2 production and photocatalytic CO2 reduction are reviewed. A thorough assessment of the progress made in photocatalysis may open new opportunities in designing highly effective hierarchical photocatalysts for advanced applications ranging from thermal catalysis, separation and purification processes to solar cells.

  7. Longitudinal hierarchical linear modeling analyses of California Psychological Inventory data from age 33 to 75: an examination of stability and change in adult personality.

    Science.gov (United States)

    Jones, Constance J; Livson, Norman; Peskin, Harvey

    2003-06-01

    Twenty aspects of personality assessed via the California Psychological Inventory (CPI; Gough & Bradley, 1996) from age 33 to 75 were examined in a sample of 279 individuals. Oakland Growth Study and Berkeley Guidance Study members completed the CPI a maximum of 4 times. We used longitudinal hierarchical linear modeling (HLM) to ask the following: Which personality characteristics change and which do not? Five CPI scales showed uniform lack of change, 2 showed heterogeneous change giving an averaged lack of change, 4 showed linear increases with age, 2 showed linear decreases with age, 4 showed gender or sample differences in linear change, 1 showed a quadratic peak, and 2 showed a quadratic nadir. The utility of HLM becomes apparent in portraying the complexity of personality change and stability.

  8. Cluster Physics with Merging Galaxy Clusters

    Directory of Open Access Journals (Sweden)

    Sandor M. Molnar

    2016-02-01

    Full Text Available Collisions between galaxy clusters provide a unique opportunity to study matter in a parameter space which cannot be explored in our laboratories on Earth. In the standard LCDM model, where the total density is dominated by the cosmological constant ($Lambda$ and the matter density by cold dark matter (CDM, structure formation is hierarchical, and clusters grow mostly by merging.Mergers of two massive clusters are the most energetic events in the universe after the Big Bang,hence they provide a unique laboratory to study cluster physics.The two main mass components in clusters behave differently during collisions:the dark matter is nearly collisionless, responding only to gravity, while the gas is subject to pressure forces and dissipation, and shocks and turbulenceare developed during collisions. In the present contribution we review the different methods used to derive the physical properties of merging clusters. Different physical processes leave their signatures on different wavelengths, thusour review is based on a multifrequency analysis. In principle, the best way to analyze multifrequency observations of merging clustersis to model them using N-body/HYDRO numerical simulations. We discuss the results of such detailed analyses.New high spatial and spectral resolution ground and space based telescopeswill come online in the near future. Motivated by these new opportunities,we briefly discuss methods which will be feasible in the near future in studying merging clusters.

  9. Hierarchical video summarization

    Science.gov (United States)

    Ratakonda, Krishna; Sezan, M. Ibrahim; Crinon, Regis J.

    1998-12-01

    We address the problem of key-frame summarization of vide in the absence of any a priori information about its content. This is a common problem that is encountered in home videos. We propose a hierarchical key-frame summarization algorithm where a coarse-to-fine key-frame summary is generated. A hierarchical key-frame summary facilitates multi-level browsing where the user can quickly discover the content of the video by accessing its coarsest but most compact summary and then view a desired segment of the video with increasingly more detail. At the finest level, the summary is generated on the basis of color features of video frames, using an extension of a recently proposed key-frame extraction algorithm. The finest level key-frames are recursively clustered using a novel pairwise K-means clustering approach with temporal consecutiveness constraint. We also address summarization of MPEG-2 compressed video without fully decoding the bitstream. We also propose efficient mechanisms that facilitate decoding the video when the hierarchical summary is utilized in browsing and playback of video segments starting at selected key-frames.

  10. Hierarchical Affinity Propagation

    CERN Document Server

    Givoni, Inmar; Frey, Brendan J

    2012-01-01

    Affinity propagation is an exemplar-based clustering algorithm that finds a set of data-points that best exemplify the data, and associates each datapoint with one exemplar. We extend affinity propagation in a principled way to solve the hierarchical clustering problem, which arises in a variety of domains including biology, sensor networks and decision making in operational research. We derive an inference algorithm that operates by propagating information up and down the hierarchy, and is efficient despite the high-order potentials required for the graphical model formulation. We demonstrate that our method outperforms greedy techniques that cluster one layer at a time. We show that on an artificial dataset designed to mimic the HIV-strain mutation dynamics, our method outperforms related methods. For real HIV sequences, where the ground truth is not available, we show our method achieves better results, in terms of the underlying objective function, and show the results correspond meaningfully to geographi...

  11. 水声传感器网络簇头分层通信模式路由算法%Routing Protocol of Hierarchical Cluster-Communication Model in the Underwater Acoustic Sensor Network

    Institute of Scientific and Technical Information of China (English)

    马绅惟; 刘广钟

    2014-01-01

    Routing protocol plays a very important role in underwater acoustic sensor networks. Based on the traditional TEEN protocol, a new routing protocol named HCM-TEEN(Hierarchical Cluster-communication Model on TEEN) has been put forward. The improved algorithm sets a new threshold function on the basis of the process of cluster candidate and the cluster elimination, and then introduces a Hierarchical Cluster-communication model in the period of data transmission to optimize the routing process. The experiment by the Matlab proved that HCM-TEEN performed better than the traditional algorithm on the network lifetime and the network average residual energy.%路由协议在水声传感器网络研究领域中扮演着非常重要的角色。基于传统的TEEN协议路由算法,提出了水声传感器网络中簇头分层通信模式的路由算法(HCM-TEEN)。新算法从簇头候选与淘汰过程入手,设置新的阈值函数。在簇头确定完成后,在数据传输阶段引入簇头分层通信模式,从距离和能量的角度上优化路由选择。通过Matlab仿真实验显示, HCM-TEEN算法与传统的算法相比在网络生命周期和节点平均剩余能量上都更具优越性。

  12. Symptom cluster analyses based on symptom occurrence and severity ratings among pediatric oncology patients during myelosuppressive chemotherapy.

    Science.gov (United States)

    Baggott, Christina; Cooper, Bruce A; Marina, Neyssa; Matthay, Katherine K; Miaskowski, Christine

    2012-01-01

    Symptom cluster research is an emerging field in symptom management. The ability to identify symptom clusters that are specific to pediatric oncology patients may lead to improved understanding of symptoms' underlying mechanisms among patients of all ages. The purpose of this study, in a sample of children and adolescents with cancer who underwent a cycle of myelosuppressive chemotherapy, was to compare the number and types of symptom clusters identified using patients' ratings of symptom occurrence and symptom severity. Children and adolescents with cancer (10-18 years of age; N = 131) completed the Memorial Symptom Assessment Scale 10-18 on the day they started a cycle of myelosuppressive chemotherapy, using a 1-week recall of experiences. Symptom data based on occurrence and severity ratings were examined using exploratory factor analysis. The defined measurement model suggested by the best exploratory factor analysis model was then examined with a latent variable analysis. Three clusters were identified when symptom occurrence ratings were evaluated, which were classified as a chemotherapy sequela cluster, mood disturbance cluster, and a neuropsychological discomfort cluster. Analysis of symptom severity ratings yielded similar cluster configurations. Cluster configurations remained relatively stable between symptom occurrence and severity ratings. The evaluation of patients at a common point in the chemotherapy cycle may have contributed to these findings. Additional uniformity in symptom clusters investigations is needed to allow appropriate comparisons among studies. The dissemination of symptom cluster research methodology through publication and presentation may promote uniformity in this field.

  13. Hierarchical Cont-Bouchaud model

    CERN Document Server

    Paluch, Robert; Holyst, Janusz A

    2015-01-01

    We extend the well-known Cont-Bouchaud model to include a hierarchical topology of agent's interactions. The influence of hierarchy on system dynamics is investigated by two models. The first one is based on a multi-level, nested Erdos-Renyi random graph and individual decisions by agents according to Potts dynamics. This approach does not lead to a broad return distribution outside a parameter regime close to the original Cont-Bouchaud model. In the second model we introduce a limited hierarchical Erdos-Renyi graph, where merging of clusters at a level h+1 involves only clusters that have merged at the previous level h and we use the original Cont-Bouchaud agent dynamics on resulting clusters. The second model leads to a heavy-tail distribution of cluster sizes and relative price changes in a wide range of connection densities, not only close to the percolation threshold.

  14. Are clusters of dietary patterns and cluster membership stable over time? Results of a longitudinal cluster analysis study.

    Science.gov (United States)

    Walthouwer, Michel Jean Louis; Oenema, Anke; Soetens, Katja; Lechner, Lilian; de Vries, Hein

    2014-11-01

    Developing nutrition education interventions based on clusters of dietary patterns can only be done adequately when it is clear if distinctive clusters of dietary patterns can be derived and reproduced over time, if cluster membership is stable, and if it is predictable which type of people belong to a certain cluster. Hence, this study aimed to: (1) identify clusters of dietary patterns among Dutch adults, (2) test the reproducibility of these clusters and stability of cluster membership over time, and (3) identify sociodemographic predictors of cluster membership and cluster transition. This study had a longitudinal design with online measurements at baseline (N=483) and 6 months follow-up (N=379). Dietary intake was assessed with a validated food frequency questionnaire. A hierarchical cluster analysis was performed, followed by a K-means cluster analysis. Multinomial logistic regression analyses were conducted to identify the sociodemographic predictors of cluster membership and cluster transition. At baseline and follow-up, a comparable three-cluster solution was derived, distinguishing a healthy, moderately healthy, and unhealthy dietary pattern. Male and lower educated participants were significantly more likely to have a less healthy dietary pattern. Further, 251 (66.2%) participants remained in the same cluster, 45 (11.9%) participants changed to an unhealthier cluster, and 83 (21.9%) participants shifted to a healthier cluster. Men and people living alone were significantly more likely to shift toward a less healthy dietary pattern. Distinctive clusters of dietary patterns can be derived. Yet, cluster membership is unstable and only few sociodemographic factors were associated with cluster membership and cluster transition. These findings imply that clusters based on dietary intake may not be suitable as a basis for nutrition education interventions. Copyright © 2014 Elsevier Ltd. All rights reserved.

  15. Genetic Algorithm for Hierarchical Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Sajid Hussain

    2007-09-01

    Full Text Available Large scale wireless sensor networks (WSNs can be used for various pervasive and ubiquitous applications such as security, health-care, industry automation, agriculture, environment and habitat monitoring. As hierarchical clusters can reduce the energy consumption requirements for WSNs, we investigate intelligent techniques for cluster formation and management. A genetic algorithm (GA is used to create energy efficient clusters for data dissemination in wireless sensor networks. The simulation results show that the proposed intelligent hierarchical clustering technique can extend the network lifetime for different network deployment environments.

  16. Integrated Light Chemical Tagging Analyses of Seven M31 Outer Halo Globular Clusters from the Pan-Andromeda Archaeological Survey

    CERN Document Server

    Sakari, Charli M; Mackey, Dougal; Shetrone, Matthew D; Dotter, Aaron; Ferguson, Annette M N; Huxor, Avon

    2015-01-01

    Detailed chemical abundances are presented for seven M31 outer halo globular clusters (with projected distances from M31 greater than 30 kpc), as derived from high resolution integrated light spectra taken with the Hobby Eberly Telescope. Five of these clusters were recently discovered in the Pan-Andromeda Archaeological Survey (PAndAS)---this paper presents the first determinations of integrated Fe, Na, Mg, Ca, Ti, Ni, Ba, and Eu abundances for these clusters. Four of the target clusters (PA06, PA53, PA54, and PA56) are metal-poor ([Fe/H] < -1.5), alpha-enhanced (though they are possibly less alpha-enhanced than Milky Way stars at the 1 sigma level), and show signs of star-to-star Na and Mg variations. The other three globular clusters (H10, H23, and PA17) are more metal rich, with metallicities ranging from [Fe/H] = -1.4 to -0.9. While H23 is chemically similar to Milky Way field stars, Milky Way globular clusters, and other M31 clusters, H10 and PA17 have moderately low [Ca/Fe], compared to Milky Way fi...

  17. 1 Hierarchical Approaches to the Analysis of Genetic Diversity in ...

    African Journals Online (AJOL)

    2015-04-14

    Apr 14, 2015 ... Keywords: Genetic diversity, Hierarchical approach, Plant, Clustering,. Descriptive ... utilization) or by clustering (based on a phonetic analysis of individual ...... Improvement of Food Crop Preservatives for the next Millennium.

  18. 基于改进层次聚类的同家族变压器状态变化规律分析%Condition evolution regularity analysis of power transformer in the same family based on improved hierarchical clustering

    Institute of Scientific and Technical Information of China (English)

    李新叶; 李新芳

    2011-01-01

    Family quality default history affects the healthy condition of power transformer greatly in integrated condition assessment. And now, it is usually subjectively decided by expert's experience. A new quantitatively computing method is proposed, that is, using hierarchical clustering technology to analyze the potential evolution regularity and then computing the influence degree of family quality default history on healthy condition of power transformer. To make the clustering result more accurate, line slope distance of condition evolution is proposed as line shape similarity criterion, both data distance criterion and line slope distance criterion are used to cluster data. The experimental result shows that our method is better than traditional hierarchical clustering method, and it is more reasonable to use clustering analysis to calculate the influence degree of family quality default history on power transformer healthy condition.%在变压器状态综合评估的研究中,家族质量缺陷史对变压器健康状态有重要影响,目前多是凭专家经验主观确定.提出利用层次聚类分析技术对同家族变压器状态变化规律进行分析,根据分析结果定量计算家族质量缺陷史对变压器健康状态的影响程度.为提高聚类的准确性,提出用变压器状态变化曲线的斜率距离作为曲线形状的相似性判据,同时用曲线间点数值距离和斜率距离构成交集约束判据进行聚类.实例分析表明改进的层次聚类算法优于传统的层次聚类算法,由聚类分析结果计算家族质量缺陷史对变压器健康状态的影响得出的结果更合理.

  19. Integrated Light Chemical Abundance Analyses of 7 M31 Outer Halo Globular Clusters from the Pan-Andromeda Archaeological Survey

    Science.gov (United States)

    Sakari, Charli; Venn, Kim; Mackey, Dougal; Shetrone, Matthew D.; Dotter, Aaron L.; Wallerstein, George

    2015-01-01

    Detailed chemical abundances of globular clusters provide insight into the formation and evolution of galaxies and their globular cluster systems. This talk presents detailed chemical abundances for seven M31 outer halo globular clusters (with projected radii greater than 30 kpc), as derived from high resolution integrated light spectra. Five of these clusters were recently discovered in the Pan-Andromeda Archaeological Survey (PAndAS). The integrated abundances show that 4 of these clusters are metal-poor ([Fe/H] < -1.5) while the other 3 are more metal-rich. The most metal-poor globular clusters are α-enhanced, though 3 of the 4 are possibly less α-enhanced than MW stars (at the 1σ level). Other chemical abundance ratios ([Ba/Eu], [Eu/Ca], and [Ni/Fe]) are consistent with origins in low mass dwarf galaxies (similar to Fornax). The most metal-rich cluster ([Fe/H] ~ -1) stands out as being chemically distinct from Milky Way field stars of the same metallicity---its chemical abundance ratios agree best with the stars and clusters in the Large Magellanic Cloud (LMC) and the Sagittarius dwarf spheroidal (Sgr) than with the Milky Way field stars. The other metal-rich clusters, H10 and H23, look similar to the LMC and Milky Way field stars in all abundance ratios. These results indicate that M31's outer halo is being at least partially built up by the accretion of dwarf satellites, in agreement with previous observations.

  20. Delineation of Stenotrophomonas maltophilia isolates from cystic fibrosis patients by fatty acid methyl ester profiles and matrix-assisted laser desorption/ionization time-of-flight mass spectra using hierarchical cluster analysis and principal component analysis.

    Science.gov (United States)

    Vidigal, Pedrina Gonçalves; Mosel, Frank; Koehling, Hedda Luise; Mueller, Karl Dieter; Buer, Jan; Rath, Peter Michael; Steinmann, Joerg

    2014-12-01

    Stenotrophomonas maltophilia is an opportunist multidrug-resistant pathogen that causes a wide range of nosocomial infections. Various cystic fibrosis (CF) centres have reported an increasing prevalence of S. maltophilia colonization/infection among patients with this disease. The purpose of this study was to assess specific fingerprints of S. maltophilia isolates from CF patients (n = 71) by investigating fatty acid methyl esters (FAMEs) through gas chromatography (GC) and highly abundant proteins by matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), and to compare them with isolates obtained from intensive care unit (ICU) patients (n = 20) and the environment (n = 11). Principal component analysis (PCA) of GC-FAME patterns did not reveal a clustering corresponding to distinct CF, ICU or environmental types. Based on the peak area index, it was observed that S. maltophilia isolates from CF patients produced significantly higher amounts of fatty acids in comparison with ICU patients and the environmental isolates. Hierarchical cluster analysis (HCA) based on the MALDI-TOF MS peak profiles of S. maltophilia revealed the presence of five large clusters, suggesting a high phenotypic diversity. Although HCA of MALDI-TOF mass spectra did not result in distinct clusters predominantly composed of CF isolates, PCA revealed the presence of a distinct cluster composed of S. maltophilia isolates from CF patients. Our data suggest that S. maltophilia colonizing CF patients tend to modify not only their fatty acid patterns but also their protein patterns as a response to adaptation in the unfavourable environment of the CF lung. © 2014 The Authors.

  1. Phylogenomic analyses of clostridia and identification of novel protein signatures that are specific to the genus Clostridium sensu stricto (cluster I).

    Science.gov (United States)

    Gupta, Radhey S; Gao, Beile

    2009-02-01

    The species of Clostridium comprise a very heterogeneous assemblage of bacteria that do not form a phylogenetically coherent group. It has been proposed previously that only a subset of the species of Clostridium that form a distinct cluster in the 16S rRNA tree (cluster I) should be regarded as the true representatives of the genus Clostridium (i.e. Clostridium sensu stricto). However, this cluster is presently defined only in phylogenetic terms, and no biochemical, molecular or phenotypic characteristic is known that is unique to species from this cluster. We report here phylogenomic and comparative analyses based on sequenced clostridial genomes in an attempt to bridge this gap and to clarify the evolutionary relationships among species of clostridia. In phylogenetic trees for species of clostridia based on concatenated sequences for 37 highly conserved proteins, the species of Clostridium cluster I formed a strongly supported clade that was separated from all other clostridia by a long branch. Several other Clostridium species that are not part of this cluster grouped reliably with other species of clostridia in a number of well-resolved clades. Our comparative genomic analyses have identified three conserved indels in three highly conserved proteins (a 4 aa insert in DNA gyrase A, a 1 aa deletion in ATP synthase beta subunit and a 1 aa insert in ribosomal protein S2) that are unique to the species of Clostridium cluster I and are not found in any other bacteria. blastp searches on various proteins in the genomes of Clostridium tetani E88 and Clostridium perfringens SM101 have also identified more than 10 proteins that are found uniquely in the cluster I species. These results provide evidence that the species of Clostridium cluster I not only are phylogenetically distinct but also share many unique molecular characteristics. These newly identified molecular markers provide useful tools to define and circumscribe the genus Clostridium sensu stricto in more

  2. Hierarchical Cluster Analysis of Three-Dimensional Reconstructions of Unbiased Sampled Microglia Shows not Continuous Morphological Changes from Stage 1 to 2 after Multiple Dengue Infections in Callithrix penicillata

    Science.gov (United States)

    Diniz, Daniel G.; Silva, Geane O.; Naves, Thaís B.; Fernandes, Taiany N.; Araújo, Sanderson C.; Diniz, José A. P.; de Farias, Luis H. S.; Sosthenes, Marcia C. K.; Diniz, Cristovam G.; Anthony, Daniel C.; da Costa Vasconcelos, Pedro F.; Picanço Diniz, Cristovam W.

    2016-01-01

    It is known that microglial morphology and function are related, but few studies have explored the subtleties of microglial morphological changes in response to specific pathogens. In the present report we quantitated microglia morphological changes in a monkey model of dengue disease with virus CNS invasion. To mimic multiple infections that usually occur in endemic areas, where higher dengue infection incidence and abundant mosquito vectors carrying different serotypes coexist, subjects received once a week subcutaneous injections of DENV3 (genotype III)-infected culture supernatant followed 24 h later by an injection of anti-DENV2 antibody. Control animals received either weekly anti-DENV2 antibodies, or no injections. Brain sections were immunolabeled for DENV3 antigens and IBA-1. Random and systematic microglial samples were taken from the polymorphic layer of dentate gyrus for 3-D reconstructions, where we found intense immunostaining for TNFα and DENV3 virus antigens. We submitted all bi- or multimodal morphological parameters of microglia to hierarchical cluster analysis and found two major morphological phenotypes designated types I and II. Compared to type I (stage 1), type II microglia were more complex; displaying higher number of nodes, processes and trees and larger surface area and volumes (stage 2). Type II microglia were found only in infected monkeys, whereas type I microglia was found in both control and infected subjects. Hierarchical cluster analysis of morphological parameters of 3-D reconstructions of random and systematic selected samples in control and ADE dengue infected monkeys suggests that microglia morphological changes from stage 1 to stage 2 may not be continuous. PMID:27047345

  3. A Hierarchical Clustering Method Based on the Threshold of Semantic Feature in Big Data%大数据中一种基于语义特征阈值的层次聚类方法

    Institute of Scientific and Technical Information of China (English)

    罗恩韬; 王国军

    2015-01-01

    云计算、健康医疗、街景地图服务、推荐系统等新兴服务促使数据的种类和规模以前所未有的速度增长,数据量的激增会导致很多共性问题.例如数据的可表示,可处理和可靠性问题.如何有效处理和分析数据之间的关系,提高数据的划分效率,建立数据的聚类分析模型,已经成为学术界和企业界共同亟待解决的问题.该文提出一种基于语义特征的层次聚类方法,首先根据数据的语义特征进行训练,然后在每个子集上利用训练结果进行层次聚类,最终产生整体数据的密度中心点,提高了数据聚类效率和准确性.此方法采样复杂度低,数据分析准确,易于实现,具有良好的判定性.%The type and scale of data has been promoted with a hitherto unknown speed by the emerging services including cloud computing, health care, street view services recommendation system and so on. However, the surge in the volume of data may lead to many common problems, such as the representability, reliability and handlability of data. Therefore, how to effectively handle the relationship between the data and the analysis to improve the efficiency of classification of the data and establish the data clustering analysis model has become an academic and business problem, which needs to be solved urgently. A hierarchical clustering method based on semantic feature is proposed. Firstly, the data should be trained according to the semantic features of data, and then is used the training result to process hierarchical clustering in each subset; finally, the density center point is produced. This method can improve the efficiency and accuracy of data clustering. This algorithm is of low complexity about sampling, high accuracy of data analysis and good judgment. Furthermore, the algorithm is easy to realize.

  4. Functional annotation of hierarchical modularity.

    Directory of Open Access Journals (Sweden)

    Kanchana Padmanabhan

    Full Text Available In biological networks of molecular interactions in a cell, network motifs that are biologically relevant are also functionally coherent, or form functional modules. These functionally coherent modules combine in a hierarchical manner into larger, less cohesive subsystems, thus revealing one of the essential design principles of system-level cellular organization and function-hierarchical modularity. Arguably, hierarchical modularity has not been explicitly taken into consideration by most, if not all, functional annotation systems. As a result, the existing methods would often fail to assign a statistically significant functional coherence score to biologically relevant molecular machines. We developed a methodology for hierarchical functional annotation. Given the hierarchical taxonomy of functional concepts (e.g., Gene Ontology and the association of individual genes or proteins with these concepts (e.g., GO terms, our method will assign a Hierarchical Modularity Score (HMS to each node in the hierarchy of functional modules; the HMS score and its p-value measure functional coherence of each module in the hierarchy. While existing methods annotate each module with a set of "enriched" functional terms in a bag of genes, our complementary method provides the hierarchical functional annotation of the modules and their hierarchically organized components. A hierarchical organization of functional modules often comes as a bi-product of cluster analysis of gene expression data or protein interaction data. Otherwise, our method will automatically build such a hierarchy by directly incorporating the functional taxonomy information into the hierarchy search process and by allowing multi-functional genes to be part of more than one component in the hierarchy. In addition, its underlying HMS scoring metric ensures that functional specificity of the terms across different levels of the hierarchical taxonomy is properly treated. We have evaluated our

  5. Synchronization patterns: from network motifs to hierarchical networks

    Science.gov (United States)

    Krishnagopal, Sanjukta; Lehnert, Judith; Poel, Winnie; Zakharova, Anna; Schöll, Eckehard

    2017-03-01

    We investigate complex synchronization patterns such as cluster synchronization and partial amplitude death in networks of coupled Stuart-Landau oscillators with fractal connectivities. The study of fractal or self-similar topology is motivated by the network of neurons in the brain. This fractal property is well represented in hierarchical networks, for which we present three different models. In addition, we introduce an analytical eigensolution method and provide a comprehensive picture of the interplay of network topology and the corresponding network dynamics, thus allowing us to predict the dynamics of arbitrarily large hierarchical networks simply by analysing small network motifs. We also show that oscillation death can be induced in these networks, even if the coupling is symmetric, contrary to previous understanding of oscillation death. Our results show that there is a direct correlation between topology and dynamics: hierarchical networks exhibit the corresponding hierarchical dynamics. This helps bridge the gap between mesoscale motifs and macroscopic networks. This article is part of the themed issue 'Horizons of cybernetical physics'.

  6. 一种基于分层结构的Ad Hoc网络分簇路由协议研究%Research based on the hierarchical structure of the Ad Hoc network clustering routing protocol

    Institute of Scientific and Technical Information of China (English)

    冯永亮

    2015-01-01

    The traditional Ad Hoc network clustering routing protocol has low packet delivery ratio problem, this paper proposes a clustering routing protocol based on hierarchical structure. The advanced network layer using AODV routing protocol based backup, and the lower network layer adopts a smaller delay DSDV protocol. The simulation results show that the improved routing protocol improves the packet delivery rate, Shortening the end to end delay.%传统Ad Hoc网络分簇路由协议存在分组投递率低的问题,论文提出一种基于分层结构的分簇路由协议.高级网络层采用基于备份路由的AODV协议,而低级网络层则采用时延较小的DSDV协议.仿真结果显示,改进后的路由协议提高了分组投递率,缩短了端到端时延.

  7. Defining reference sequences for Nocardia species by similarity and clustering analyses of 16S rRNA gene sequence data.

    Directory of Open Access Journals (Sweden)

    Manal Helal

    Full Text Available BACKGROUND: The intra- and inter-species genetic diversity of bacteria and the absence of 'reference', or the most representative, sequences of individual species present a significant challenge for sequence-based identification. The aims of this study were to determine the utility, and compare the performance of several clustering and classification algorithms to identify the species of 364 sequences of 16S rRNA gene with a defined species in GenBank, and 110 sequences of 16S rRNA gene with no defined species, all within the genus Nocardia. METHODS: A total of 364 16S rRNA gene sequences of Nocardia species were studied. In addition, 110 16S rRNA gene sequences assigned only to the Nocardia genus level at the time of submission to GenBank were used for machine learning classification experiments. Different clustering algorithms were compared with a novel algorithm or the linear mapping (LM of the distance matrix. Principal Components Analysis was used for the dimensionality reduction and visualization. RESULTS: The LM algorithm achieved the highest performance and classified the set of 364 16S rRNA sequences into 80 clusters, the majority of which (83.52% corresponded with the original species. The most representative 16S rRNA sequences for individual Nocardia species have been identified as 'centroids' in respective clusters from which the distances to all other sequences were minimized; 110 16S rRNA gene sequences with identifications recorded only at the genus level were classified using machine learning methods. Simple kNN machine learning demonstrated the highest performance and classified Nocardia species sequences with an accuracy of 92.7% and a mean frequency of 0.578. CONCLUSION: The identification of centroids of 16S rRNA gene sequence clusters using novel distance matrix clustering enables the identification of the most representative sequences for each individual species of Nocardia and allows the quantitation of inter- and intra

  8. The use of principle component and cluster analyses to differentiate banana pulp flours based on starch and dietary fiber components.

    Science.gov (United States)

    Ramli, Saifullah Bin; Alkarkhi, Abbas F M; Yong, Yeoh Shin; Easa, Azhar Mat

    2009-01-01

    Flour prepared from green and ripe Cavendish and Dream banana fruits were assessed for total starch, digestible starch, resistant starch, total dietary fiber, soluble dietary fiber and insoluble dietary fiber. Principle component analysis identified only one component responsible for explaining 83.83% of the total variance in the starch and dietary fiber components data to indicate that ripe banana flour had different characteristics from the green. Cluster analysis applied on similar data obtained two statistically significant clusters of green and ripe banana to indicate difference in behaviors according to the stages of ripeness. In conclusion, starch and dietary fiber components could be used to discriminate between flour prepared from fruits of different stage of ripeness. Results are also suggestive of the potential of green as well as the ripe banana flour as functional ingredients in food.

  9. Quantitative binomial distribution analyses of nanoscale like-solute atom clustering and segregation in atom probe tomography data.

    Science.gov (United States)

    Moody, Michael P; Stephenson, Leigh T; Ceguerra, Anna V; Ringer, Simon P

    2008-07-01

    The applicability of the binomial frequency distribution is outlined for the analysis of the evolution nanoscale atomic clustering of dilute solute in an alloy subject to thermal ageing in 3D atom probe data. The conventional chi(2) statistics and significance testing are demonstrated to be inappropriate for comparison of quantity of solute segregation present in two or more different sized system. Pearson coefficient, mu, is shown to normalize chi(2) with respect to sample size over an order of magnitude. A simple computer simulation is implemented to investigate the binomial analysis and infer meaning in the measured value of mu over a series of systems at different solute concentrations and degree of clustering. The simulations replicate the form of experimental data and demonstrate the effect of detector efficiency to significantly underestimate the measured segregation. The binomial analysis is applied to experimental atom probe data sets and complementary simulations are used to interpret the results.

  10. Complexity of major UK companies between 2006 and 2010: Hierarchical structure method approach

    Science.gov (United States)

    Ulusoy, Tolga; Keskin, Mustafa; Shirvani, Ayoub; Deviren, Bayram; Kantar, Ersin; Çaǧrı Dönmez, Cem

    2012-11-01

    This study reports on topology of the top 40 UK companies that have been analysed for predictive verification of markets for the period 2006-2010, applying the concept of minimal spanning tree and hierarchical tree (HT) analysis. Construction of the minimal spanning tree (MST) and the hierarchical tree (HT) is confined to a brief description of the methodology and a definition of the correlation function between a pair of companies based on the London Stock Exchange (LSE) index in order to quantify synchronization between the companies. A derivation of hierarchical organization and the construction of minimal-spanning and hierarchical trees for the 2006-2008 and 2008-2010 periods have been used and the results validate the predictive verification of applied semantics. The trees are known as useful tools to perceive and detect the global structure, taxonomy and hierarchy in financial data. From these trees, two different clusters of companies in 2006 were detected. They also show three clusters in 2008 and two between 2008 and 2010, according to their proximity. The clusters match each other as regards their common production activities or their strong interrelationship. The key companies are generally given by major economic activities as expected. This work gives a comparative approach between MST and HT methods from statistical physics and information theory with analysis of financial markets that may give new valuable and useful information of the financial market dynamics.

  11. Incidence analyses and space-time cluster detection of hepatitis C in Fujian Province of China from 2006 to 2010.

    Directory of Open Access Journals (Sweden)

    Shunquan Wu

    Full Text Available BACKGROUND: There is limited epidemiologic information about the incidence of hepatitis C in China, and few studies have applied space-time scan statistic to detect clusters of hepatitis C and made adjustment for temporal trend and relative risk of regions. METHODOLOGY AND PRINCIPAL FINDINGS: We analyzed the temporal changes and characteristics of incidence of hepatitis C in Fujian Province from 2006 through 2010. The discrete Poisson model of space-time scan statistic was chosen for cluster detection. Data on new cases of hepatitis C were obtained from the Center for Disease Control and Prevention of Fujian Province. Between 2006 and 2010, there was an annualized increase in the incidence of hepatitis C of 23.0 percent, from 928 cases (2.63 per 100,000 persons to 2,180 cases (6.01 per 100,000 persons. The incidence among women increased more rapidly. The cumulative incidence showed that people who were over 60 years had the highest risk to suffer hepatitis C (52.51 per 100,000 persons, and women had lower risk compared to men (OR=0.69. Putian had the highest cumulative incidence among all the regions (86.95 per 100,000 persons. The most likely cluster was identified in Putian during March to August in 2009 without adjustment, but it shifted to three contiguous cities with a two-month duration after adjustment for temporal trend and relative risk of regions. CONCLUSIONS/SIGNIFICANCE: The incidence of hepatitis C is increasing in Fujian Province, and women are at a more rapid pace. The space-time scan statistic is useful as a screening tool for clusters of hepatitis C, with adjustment for temporal trend and relative risk of regions recommended.

  12. CLASH-VLT: constraints on f(R) gravity models with galaxy clusters using lensing and kinematic analyses

    Science.gov (United States)

    Pizzuti, L.; Sartoris, B.; Amendola, L.; Borgani, S.; Biviano, A.; Umetsu, K.; Mercurio, A.; Rosati, P.; Balestra, I.; Caminha, G. B.; Girardi, M.; Grillo, C.; Nonino, M.

    2017-07-01

    We perform a maximum likelihood kinematic analysis of the two dynamically relaxed galaxy clusters MACS J1206.2-0847 at z=0.44 and RXC J2248.7-4431 at z=0.35 to determine the total mass profile in modified gravity models, using a modified version of the MAMPOSSt code of Mamon, Biviano and Bou&apose. Our work is based on the kinematic and lensing mass profiles derived using the data from the Cluster Lensing And Supernova survey with Hubble (hereafter CLASH) and the spectroscopic follow-up with the Very Large Telescope (hereafter CLASH-VLT). We assume a spherical Navarro-Frenk-White (NFW hereafter) profile in order to obtain a constraint on the fifth force interaction range λ for models in which the dependence of this parameter on the environment is negligible at the scale considered (i.e. λ=const) and fixing the fifth force strength to the value predicted in f(R) gravity. We then use information from lensing analysis to put a prior on the other NFW free parameters. In the case of MACSJ 1206 the joint kinematic+lensing analysis leads to an upper limit on the effective interaction range λdistribution. For RXJ 2248 instead a possible tension with the ΛCDM model appears when adding lensing information, with a lower limit λ>=0.14 mpc at Δχ2=2.71. This is consequence of the slight difference between the lensing and kinematic data, appearing in GR for this cluster, that could in principle be explained in terms of modifications of gravity. We discuss the impact of systematics and the limits of our analysis as well as future improvements of the results obtained. This work has interesting implications in view of upcoming and future large imaging and spectroscopic surveys, that will deliver lensing and kinematic mass reconstruction for a large number of galaxy clusters.

  13. Cluster analysis for applications

    CERN Document Server

    Anderberg, Michael R

    1973-01-01

    Cluster Analysis for Applications deals with methods and various applications of cluster analysis. Topics covered range from variables and scales to measures of association among variables and among data units. Conceptual problems in cluster analysis are discussed, along with hierarchical and non-hierarchical clustering methods. The necessary elements of data analysis, statistics, cluster analysis, and computer implementation are integrated vertically to cover the complete path from raw data to a finished analysis.Comprised of 10 chapters, this book begins with an introduction to the subject o

  14. High-performance supercapacitor and lithium-ion battery based on 3D hierarchical NH4F-induced nickel cobaltate nanosheet-nanowire cluster arrays as self-supported electrodes

    Science.gov (United States)

    Chen, Yuejiao; Qu, Baihua; Hu, Lingling; Xu, Zhi; Li, Qiuhong; Wang, Taihong

    2013-09-01

    A facile hydrothermal method is developed for large-scale production of three-dimensional (3D) hierarchical porous nickel cobaltate nanowire cluster arrays derived from nanosheet arrays with robust adhesion on Ni foam. Based on the morphology evolution upon reaction time, a possible formation process is proposed. The role of NH4F in formation of the structure has also been investigated based on different NH4F amounts. This unique structure significantly enhances the electroactive surface areas of the NiCo2O4 arrays, leading to better interfacial/chemical distributions at the nanoscale, fast ion and electron transfer and good strain accommodation. Thus, when it is used for supercapacitor testing, a specific capacitance of 1069 F g-1 at a very high current density of 100 A g-1 was obtained. Even after more than 10 000 cycles at various large current densities, a capacitance of 2000 F g-1 at 10 A g-1 with 93.8% retention can be achieved. It also exhibits a high-power density (26.1 kW kg-1) at a discharge current density of 80 A g-1. When used as an anode material for lithium-ion batteries (LIBs), it presents a high reversible capacity of 976 mA h g-1 at a rate of 200 mA g-1 with good cycling stability and rate capability. This array material is rarely used as an anode material. Our results show that this unique 3D hierarchical porous nickel cobaltite is promising for electrochemical energy applications.A facile hydrothermal method is developed for large-scale production of three-dimensional (3D) hierarchical porous nickel cobaltate nanowire cluster arrays derived from nanosheet arrays with robust adhesion on Ni foam. Based on the morphology evolution upon reaction time, a possible formation process is proposed. The role of NH4F in formation of the structure has also been investigated based on different NH4F amounts. This unique structure significantly enhances the electroactive surface areas of the NiCo2O4 arrays, leading to better interfacial/chemical distributions

  15. High-performance supercapacitor and lithium-ion battery based on 3D hierarchical NH4F-induced nickel cobaltate nanosheet-nanowire cluster arrays as self-supported electrodes.

    Science.gov (United States)

    Chen, Yuejiao; Qu, Baihua; Hu, Lingling; Xu, Zhi; Li, Qiuhong; Wang, Taihong

    2013-10-21

    A facile hydrothermal method is developed for large-scale production of three-dimensional (3D) hierarchical porous nickel cobaltate nanowire cluster arrays derived from nanosheet arrays with robust adhesion on Ni foam. Based on the morphology evolution upon reaction time, a possible formation process is proposed. The role of NH4F in formation of the structure has also been investigated based on different NH4F amounts. This unique structure significantly enhances the electroactive surface areas of the NiCo2O4 arrays, leading to better interfacial/chemical distributions at the nanoscale, fast ion and electron transfer and good strain accommodation. Thus, when it is used for supercapacitor testing, a specific capacitance of 1069 F g(-1) at a very high current density of 100 A g(-1) was obtained. Even after more than 10,000 cycles at various large current densities, a capacitance of 2000 F g(-1) at 10 A g(-1) with 93.8% retention can be achieved. It also exhibits a high-power density (26.1 kW kg(-1)) at a discharge current density of 80 A g(-1). When used as an anode material for lithium-ion batteries (LIBs), it presents a high reversible capacity of 976 mA h g(-1) at a rate of 200 mA g(-1) with good cycling stability and rate capability. This array material is rarely used as an anode material. Our results show that this unique 3D hierarchical porous nickel cobaltite is promising for electrochemical energy applications.

  16. Weighted Clustering

    CERN Document Server

    Ackerman, Margareta; Branzei, Simina; Loker, David

    2011-01-01

    In this paper we investigate clustering in the weighted setting, in which every data point is assigned a real valued weight. We conduct a theoretical analysis on the influence of weighted data on standard clustering algorithms in each of the partitional and hierarchical settings, characterising the precise conditions under which such algorithms react to weights, and classifying clustering methods into three broad categories: weight-responsive, weight-considering, and weight-robust. Our analysis raises several interesting questions and can be directly mapped to the classical unweighted setting.

  17. Comparison of KENO-VI and MCNP5 Criticality Analyses for a Lunar Regolith Clustered-Reactor System

    Science.gov (United States)

    Bess, John Darrell

    2008-01-01

    The Lunar Regolith Clustered-Reactor System design has been presented as an alternative method for providing surface power to a lunar facility using a fast-fission, heatpipe-cooled nuclear reactor. The reactor system is divided into subcritical units that can be safely launched into orbit without risk of inadvertent criticality in the event of a launch accident. The reactor subunits are emplaced into the lunar surface to form a clustered-reactor system, utilizing the regolith as both radiation shielding and neutron-reflector material. Coordinated placement of multiple subunits can provision a critical reactor system proportional to localized lunar surface power demand. Reactor units assembled using proven and tested materials in radiation environments such as UO2 fuel, stainless-steel cladding and support, and compatible liquid-metal heatpipes promote safety and reliability, with ease of manufacture and testing. Reactor power levels of approximately 100 kWth per subunit significantly reduces the negative effects of elevated temperature and radiation environments associated with single nuclear power reactors operated at higher power levels. The analysis of subunit criticality in various accident scenarios differs by up to 4% (~$6 in reactivity) between results generated using conventional criticality analysis codes, MCNP5 and KENO-VI. A demonstrated trend exists between results of the two criticality codes as accident conditions approach a multiplication factor of one. Code comparison of a tri-cluster system on the lunar surface provides comparable results with calculated system reactivity within 0.5%. Iron concentration is confirmed as the dominant element in the lunar regolith influencing system reactivity.

  18. Identifying Unique Neighborhood Characteristics to Guide Health Planning for Stroke and Heart Attack: Fuzzy Cluster and Discriminant Analyses Approaches

    Science.gov (United States)

    Pedigo, Ashley; Seaver, William; Odoi, Agricola

    2011-01-01

    Background Socioeconomic, demographic, and geographic factors are known determinants of stroke and myocardial infarction (MI) risk. Clustering of these factors in neighborhoods needs to be taken into consideration during planning, prioritization and implementation of health programs intended to reduce disparities. Given the complex and multidimensional nature of these factors, multivariate methods are needed to identify neighborhood clusters of these determinants so as to better understand the unique neighborhood profiles. This information is critical for evidence-based health planning and service provision. Therefore, this study used a robust multivariate approach to classify neighborhoods and identify their socio-demographic characteristics so as to provide information for evidence-based neighborhood health planning for stroke and MI. Methods and Findings The study was performed in East Tennessee Appalachia, an area with one of the highest stroke and MI risks in USA. Robust principal component analysis was performed on neighborhood (census tract) socioeconomic and demographic characteristics, obtained from the US Census, to reduce the dimensionality and influence of outliers in the data. Fuzzy cluster analysis was used to classify neighborhoods into Peer Neighborhoods (PNs) based on their socioeconomic and demographic characteristics. Nearest neighbor discriminant analysis and decision trees were used to validate PNs and determine the characteristics important for discrimination. Stroke and MI mortality risks were compared across PNs. Four distinct PNs were identified and their unique characteristics and potential health needs described. The highest risk of stroke and MI mortality tended to occur in less affluent PNs located in urban areas, while the suburban most affluent PNs had the lowest risk. Conclusions Implementation of this multivariate strategy provides health planners useful information to better understand and effectively plan for the unique

  19. Extended clustering analyses to constrain the deflection angular scale and source density of the ultra-high-energy cosmic rays

    CERN Document Server

    Decerprit, Guillaume; Parizot, Etienne

    2011-01-01

    The search of a clustering signal in the arrival directions of ultra-high-energy cosmic rays (UHECRs) is a standard method to assess the level of anisotropy of the data sets under investigation. Here, we first show how to quantify the sensitivity of a UHECR detector to the detection of anisotropy, and then propose a new method that pushes forward the study of the two-point auto-correlation function, enabling one to put astrophysically meaningful constraints on both the effective UHECR source density and the angular deflections that these charged particles suffer while they propagate through the galactic and intergalactic magnetic fields. We apply the method to simulated data sets obtained under various astrophysical conditions, and show how the input model parameters can be estimated through our analysis, introducing the notion of "clustering similarity" (between data sets), to which we give a precise statistical meaning. We also study how the constraining power of the method is influenced by the size of the ...

  20. Unsupervised clustering analyses of features extraction for a caries computer-assisted diagnosis using dental fluorescence images

    Science.gov (United States)

    Bessani, Michel; da Costa, Mardoqueu M.; Lins, Emery C. C. C.; Maciel, Carlos D.

    2014-02-01

    Computer-assisted diagnoses (CAD) are performed by systems with embedded knowledge. These systems work as a second opinion to the physician and use patient data to infer diagnoses for health problems. Caries is the most common oral disease and directly affects both individuals and the society. Here we propose the use of dental fluorescence images as input of a caries computer-assisted diagnosis. We use texture descriptors together with statistical pattern recognition techniques to measure the descriptors performance for the caries classification task. The data set consists of 64 fluorescence images of in vitro healthy and carious teeth including different surfaces and lesions already diagnosed by an expert. The texture feature extraction was performed on fluorescence images using RGB and YCbCr color spaces, which generated 35 different descriptors for each sample. Principal components analysis was performed for the data interpretation and dimensionality reduction. Finally, unsupervised clustering was employed for the analysis of the relation between the output labeling and the diagnosis of the expert. The PCA result showed a high correlation between the extracted features; seven components were sufficient to represent 91.9% of the original feature vectors information. The unsupervised clustering output was compared with the expert classification resulting in an accuracy of 96.88%. The results show the high accuracy of the proposed approach in identifying carious and non-carious teeth. Therefore, the development of a CAD system for caries using such an approach appears to be promising.

  1. Community detection algorithm based on hierarchical clustering under signal missing in propagating process%传播过程中信号缺失的层次聚类社区发现算法

    Institute of Scientific and Technical Information of China (English)

    康茜; 李德玉; 王素格; 冀庆斌

    2015-01-01

    社区发现是社会网络分析的一个基本任务,而社区结构探测是社区发现的一个关键问题。将社区结构中的结点看作信号源,针对信号传递过程中存在信号缺失情况,提出了一种层次聚类社区发现算法。该算法通过度中心性来度量节点接收信号的概率,用于量化节点接受信号过程中的缺失值。经过信号传递,使网络的拓扑结构转化为向量间的几何关系,在此基础上,使用层次聚类算法用于发现社区。为了验证SMHC算法的有效性,通过在三个数据集上与SHC算法、CNM算法、GN算法、Similar算法进行比较,实验结果表明,SMHC算法在一定程度上提高了社区发现的正确率。%Community identification is a basic task of social network analysis, meanwhile the community structure detec-tion is a key problem of community identification. Each node in the community structure is regarded as the signal source. A hierarchical clustering community algorithm is proposed in order to settle the problem of signal missing in the process of signal transmission. The algorithm measures the probability of receiving signals of nodes by degree centrality to quantify the signal missing values. After the signal transmission, the topology of the network is transformed into geometric relation-ships among the vectors. On the basis, the hierarchical clustering algorithm is used to find the community structure. In order to validate the proposed method, this paper compares it with SHC algorithm, CNM algorithm, GN algorithm and Similar algorithm. Under three real networks, the Zachary Club, American Football and Netscience, the experimental results indi-cate that SMHC algorithm can effectively improve precision.

  2. Statistical properties of convex clustering

    OpenAIRE

    Tan, Kean Ming; Witten, Daniela

    2015-01-01

    In this manuscript, we study the statistical properties of convex clustering. We establish that convex clustering is closely related to single linkage hierarchical clustering and $k$-means clustering. In addition, we derive the range of the tuning parameter for convex clustering that yields a non-trivial solution. We also provide an unbiased estimator of the degrees of freedom, and provide a finite sample bound for the prediction error for convex clustering. We compare convex clustering to so...

  3. Block-based logical hierarchical cluster for distributed multimedia architecture on demand server%基于块的逻辑层次集群:一种分布式多媒体点播服务器的体系结构

    Institute of Scientific and Technical Information of China (English)

    熊旭辉; 余胜生; 周敬利

    2006-01-01

    A structure of logical hierarchical cluster for the distributed multimedia on demand server is proposed. The architecture is mainly composed of the network topology and the resource management of all server nodes. Instead of the physical network hierarchy or the independent management hierarchy, the nodes are organized into a logically hierarchical cluster according to the multimedia block they caches in the midderware layer. The process of a member joining/leaving or the structure adjustment cooperatively implemented by all members is concerned with decentralized maintenance of the logical cluster hierarchy. As the root of each logically hierarchical cluster is randomly mapped into the system, the logical structure of a multimedia block is dynamically expanded across some regions by the two replication policies in different load state respectively. The local load diversion is applied to fine-tune the load of nodes within a local region but belongs to different logical hierarchies. Guaranteed by the dynamic expansion of a logical structure and the load diversion of a local region, the users always select a closest idle node from the logical hierarchy under the condition of topology integration with resource management.

  4. Hierarchical Star Formation Across Galactic Disks

    Science.gov (United States)

    Gouliermis, Dimitrios

    2016-09-01

    Most stars form in clusters. This fact has emerged from the finding that "embedded clusters account for the 70 - 90% fraction of all stars formed in Giant Molecular Clouds (GMCs)." While this is the case at scales of few 10 parsecs, typical for GMCs, a look at star-forming galaxies in the Local Group (LG) shows significant populations of enormous loose complexes of early-type stars extending at scales from few 100 to few 1000 parsecs. The fact that these stellar complexes host extremely large numbers of loosely distributed massive blue stars implies either that stars form also in an unbound fashion or they are immediately dislocated from their original compact birthplaces or both. The Legacy Extra-Galactic UV Survey (LEGUS) has produced remarkable collections of resolved early-type stars in 50 star-forming LG galaxies, suited for testing ideas about recent star formation. I will present results from our ongoing project on star formation across LEGUS disk galaxies. We characterize the global clustering behavior of the massive young stars in order to understand the morphology of star formation over galactic scales. This morphology appears to be self-similar with fractal dimensions comparable to those of the molecular interstellar medium, apparently driven by large-scale turbulence. Our clustering analysis reveals compact stellar systems nested in larger looser concentrations, which themselves are the dense parts of unbound complexes and super-structures, giving evidence of hierarchical star formation up to galactic scales. We investigate the structural and star formation parameters demographics of the star-forming complexes revealed at various levels of compactness. I will discuss the outcome of our correlation and regression analyses on these parameters in an attempt to understand the link between galactic disk dynamics and morphological structure in spiral and ring galaxies of the local universe.

  5. Methods for analyzing cost effectiveness data from cluster randomized trials

    Directory of Open Access Journals (Sweden)

    Clark Allan

    2007-09-01

    Full Text Available Abstract Background Measurement of individuals' costs and outcomes in randomized trials allows uncertainty about cost effectiveness to be quantified. Uncertainty is expressed as probabilities that an intervention is cost effective, and confidence intervals of incremental cost effectiveness ratios. Randomizing clusters instead of individuals tends to increase uncertainty but such data are often analysed incorrectly in published studies. Methods We used data from a cluster randomized trial to demonstrate five appropriate analytic methods: 1 joint modeling of costs and effects with two-stage non-parametric bootstrap sampling of clusters then individuals, 2 joint modeling of costs and effects with Bayesian hierarchical models and 3 linear regression of net benefits at different willingness to pay levels using a least squares regression with Huber-White robust adjustment of errors, b a least squares hierarchical model and c a Bayesian hierarchical model. Results All five methods produced similar results, with greater uncertainty than if cluster randomization was not accounted for. Conclusion Cost effectiveness analyses alongside cluster randomized trials need to account for study design. Several theoretically coherent methods can be implemented with common statistical software.

  6. Comparative phylogenomics and multi-gene cluster analyses of the Citrus Huanglongbing (HLB-associated bacterium Candidatus Liberibacter

    Directory of Open Access Journals (Sweden)

    Civerolo Edwin L

    2008-08-01

    Full Text Available Abstract Background Huanglongbing (HLB, previously known as citrus greening, is associated with Candidatus Liberibacter species and is a serious threat to citrus production world-wide. The pathogen is a Gram negative, unculturable, phloem-limited bacterium with limited known genomic information. Expanding the genetic knowledge of this organism may provide better understanding of the pathogen and possibly develop effective strategies for control and management of HLB. Results Here, we report cloning and characterization of an additional 14.7 Kb of new genomic sequences from three different genomic regions of the Candidatus Liberibacter asiaticus (Las. Sequence variation analyses among the available Ca. Liberibacter species sequences as well as the newly cloned 1.5 Kb of rpoB gene from different Ca. Liberibacter strains have identified INDELs and SNPs. Phylogenetic analysis of the deduced protein sequences from the cloned regions characterizes the HLB-associated Candidatus Liberibacter as a new clade in the sub-division of the α-proteobacteria. Conclusion Comparative analyses of the cloned gene regions of Candidatus Liberibacter with members of the order Rhizobiales suggest overall gene structure and order conservation, albeit with minor variations including gene decay due to the identified pseudogenes. The newly cloned gene regions contribute to our understanding of the molecular aspects of genomic evolution of Ca. Liberibacter.

  7. Hierarchical Network Design

    DEFF Research Database (Denmark)

    Thomadsen, Tommy

    2005-01-01

    of different types of hierarchical networks. This is supplemented by a review of ring network design problems and a presentation of a model allowing for modeling most hierarchical networks. We use methods based on linear programming to design the hierarchical networks. Thus, a brief introduction to the various....... The thesis investigates models for hierarchical network design and methods used to design such networks. In addition, ring network design is considered, since ring networks commonly appear in the design of hierarchical networks. The thesis introduces hierarchical networks, including a classification scheme...... linear programming based methods is included. The thesis is thus suitable as a foundation for study of design of hierarchical networks. The major contribution of the thesis consists of seven papers which are included in the appendix. The papers address hierarchical network design and/or ring network...

  8. Optimizd Design of Power Scheduling in WSN Based on Sink Root Data Tree with Hierarchical Clustering%Sink根数据聚集树分层的WSN电力调度优化设计

    Institute of Scientific and Technical Information of China (English)

    朱文忠

    2014-01-01

    为提高电力数据调度效率,缩短电力数据调度延时,提出一种改进的无通信冲突的分布式电力数据聚集调度近似算法,采用Sink根数据聚集树对无线传感器网络中各个节点电力资源数据进行分层数据调度,根据分布式数据集对各个电力节点之间的控制信息进行不断融合处理,在最大独立集的基础上建立一棵根在Sink的数据聚集树。每个节点分配一个时间片,使该节点能在无通信冲突的情况下传输数据。仿真实验表明,采用改进算法得到的聚集延时明显减小,有效保证了电力调度控制的实时性,电力信息数据分层融合度能达到90%以上,而改进前的算法只有10%~50%之间。%In order to improve the power data scheduling efficiency, shorten the power data scheduling delay, and improve matching and integration degree, and improved power scheduling optimization design method based on Sink root data tree hierarchical clustering was proposed for improve the management efficiency. We established a tree root in the Sink data ag-gregation tree based on the maximum independent set. Each node was assigned a time slice, so that the node could transmit data in the absence of communication conflict situations. Simulation results show that the improved algorithm has signifi-cantly reduced aggregation delay, and it has effectively ensured the real-time dispatching control, and the data hierarchical fusion degree can reach more than 90%, while the former algorithm is only 10%~50%.

  9. Hierarchical Multiagent Reinforcement Learning

    Science.gov (United States)

    2004-01-25

    In this paper, we investigate the use of hierarchical reinforcement learning (HRL) to speed up the acquisition of cooperative multiagent tasks. We...introduce a hierarchical multiagent reinforcement learning (RL) framework and propose a hierarchical multiagent RL algorithm called Cooperative HRL. In

  10. Population structure of Atlantic Mackerel inferred from RAD-seq derived SNP markers: effects of sequence clustering parameters and hierarchical SNP selection

    KAUST Repository

    Rodríguez-Ezpeleta, Naiara

    2016-03-03

    Restriction-site associated DNA sequencing (RAD-seq) and related methods are revolutionizing the field of population genomics in non-model organisms as they allow generating an unprecedented number of single nucleotide polymorphisms (SNPs) even when no genomic information is available. Yet, RAD-seq data analyses rely on assumptions on nature and number of nucleotide variants present in a single locus, the choice of which may lead to an under- or overestimated number of SNPs and/or to incorrectly called genotypes. Using the Atlantic mackerel (Scomber scombrus L.) and a close relative, the Atlantic chub mackerel (Scomber colias), as case study, here we explore the sensitivity of population structure inferences to two crucial aspects in RAD-seq data analysis: the maximum number of mismatches allowed to merge reads into a locus and the relatedness of the individuals used for genotype calling and SNP selection. Our study resolves the population structure of the Atlantic mackerel, but, most importantly, provides insights into the effects of alternative RAD-seq data analysis strategies on population structure inferences that are directly applicable to other species.

  11. Generation of hierarchically correlated multivariate symbolic sequences

    CERN Document Server

    Tumminello, Mi; Mantegna, R N

    2008-01-01

    We introduce an algorithm to generate multivariate series of symbols from a finite alphabet with a given hierarchical structure of similarities. The target hierarchical structure of similarities is arbitrary, for instance the one obtained by some hierarchical clustering procedure as applied to an empirical matrix of Hamming distances. The algorithm can be interpreted as the finite alphabet equivalent of the recently introduced hierarchically nested factor model (M. Tumminello et al. EPL 78 (3) 30006 (2007)). The algorithm is based on a generating mechanism that is different from the one used in the mutation rate approach. We apply the proposed methodology for investigating the relationship between the bootstrap value associated with a node of a phylogeny and the probability of finding that node in the true phylogeny.

  12. Stratification and analysis of housing indicators of rural areas of Isfahan province using factor and cluster analyses

    Directory of Open Access Journals (Sweden)

    S. E. Seidaiy

    2013-01-01

    : market economy and planned economy. In view of market economy, housing problems are solved through the market mechanisms and housing needs are provided by private sector (Chadwick, 1987:88, Ziyari, et al., 210:4. In planned economy government has the role of planner, designer and manager (Aghasi, 1996:201, Chadwick, 1987:88, Shucksmith, 2003:213. In Islam's ideological system the importance of housing is as far as that the housing provision is considered as one of the bases of economic independency, and eradication of poverty in the society.3– DiscussionTo evaluate and analyze the housing indicators in the rural areas of Isfahan province, first data and the related variables are collected and based on them the desired indicators are obtained (Table-1; then, in line with goals of research, we will go through the following steps:Analysis of housing situation in rural areas of Isfahan province by using housing indicators,Determining effective factors in improving housing indicators,And stratification of rural areas based on these indicators.Applying statistical techniques (factor analysis and cluster analysis, analysis of indicators and prioritization of rural areas of the province are performed. Table 1: Housing IndicatorsROWindicatorsROWindicators1The population of rural areas12The average of infrastructure lifetime2The number of households13The share of households that have a minimum electricity4The family size14The share of households that have a minimum telephone4The number of residential units,15The share of households that have a minimum water piping5The household density in residential units16The share of households that have a minimum gas piping6The density of people in residential units17The share of households that have a minimum central heating and cooling system7The housing shortages18The share of households that have a minimum kitchen8The average of number of rooms in the household19The share of households that have a minimum bathroom9The average of number

  13. Cluster-cluster clustering

    Science.gov (United States)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C. S.

    1985-01-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales.

  14. Cluster-cluster clustering

    Energy Technology Data Exchange (ETDEWEB)

    Barnes, J.; Dekel, A.; Efstathiou, G.; Frenk, C.S.

    1985-08-01

    The cluster correlation function xi sub c(r) is compared with the particle correlation function, xi(r) in cosmological N-body simulations with a wide range of initial conditions. The experiments include scale-free initial conditions, pancake models with a coherence length in the initial density field, and hybrid models. Three N-body techniques and two cluster-finding algorithms are used. In scale-free models with white noise initial conditions, xi sub c and xi are essentially identical. In scale-free models with more power on large scales, it is found that the amplitude of xi sub c increases with cluster richness; in this case the clusters give a biased estimate of the particle correlations. In the pancake and hybrid models (with n = 0 or 1), xi sub c is steeper than xi, but the cluster correlation length exceeds that of the points by less than a factor of 2, independent of cluster richness. Thus the high amplitude of xi sub c found in studies of rich clusters of galaxies is inconsistent with white noise and pancake models and may indicate a primordial fluctuation spectrum with substantial power on large scales. 30 references.

  15. Application of cluster and discriminant analyses to diagnose lithological heterogeneity of the parent material according to its particle-size distribution

    Science.gov (United States)

    Giniyatullin, K. G.; Valeeva, A. A.; Smirnova, E. V.

    2017-08-01

    Particle-size distribution in soddy-podzolic and light gray forest soils of the Botanical Garden of Kazan Federal University has been studied. The cluster analysis of data on the samples from genetic soil horizons attests to the lithological heterogeneity of the profiles of all the studied soils. It is probable that they are developed from the two-layered sediments with the upper colluvial layer underlain by the alluvial layer. According to the discriminant analysis, the major contribution to the discrimination of colluvial and alluvial layers is that of the fraction >0.25 mm. The results of canonical analysis show that there is only one significant discriminant function that separates alluvial and colluvial sediments on the investigated territory. The discriminant function correlates with the contents of fractions 0.05-0.01, 0.25-0.05, and >0.25 mm. Classification functions making it possible to distinguish between alluvial and colluvial sediments have been calculated. Statistical assessment of particle-size distribution data obtained for the plow horizons on ten plowed fields within the garden indicates that this horizon is formed from colluvial sediments. We conclude that the contents of separate fractions and their ratios cannot be used as a universal criterion of the lithological heterogeneity. However, adequate combination of the cluster and discriminant analyses makes it possible to give a comprehensive assessment of the lithology of soil samples from data on the contents of sand and silt fractions, which considerably increases the information value and reliability of the results.

  16. The Grism Lens-Amplified Survey from Space (GLASS). IX. The dual origin of low-mass cluster galaxies as revealed by new structural analyses

    CERN Document Server

    Morishita, Takahiro; Treu, Tommaso; Vulcani, Benedetta; Schmidt, Kasper B; Dressler, Alan; Poggianti, Bianca; Malkan, Matthew A; Wang, Xin; Huang, Kuang-Han; Trenti, Michele; Bradac, Marusa; Hoag, Austin

    2016-01-01

    Using deep Hubble Frontier Field imaging and slitless spectroscopy from the Grism Lens-Amplified Survey from Space, we analyze 2200 cluster and 1748 field galaxies at $0.2\\leq z\\leq0.7$ to determine the impact of environment on galaxy size and structure at $\\log M_*/M_\\odot>7.8$, an unprecedented limit at these redshifts. Based on both simple--$r_e= f(M_*)$--and more complex analyses--$r_e = f(M_*, C, n, z,\\Sigma)$--we find local density ($\\Sigma$) to induce a $7\\%\\pm3\\%$ ($95\\%$ confidence) reduction in half-light radii ($r_e$) beyond what can be accounted for by stellar mass ($M_*$), $U-V$ color ($C$), S\\'ersic index ($n$), and redshift ($z$) effects. Almost any size difference between galaxies in high- and low-density regions is thus attributable to their different distributions in properties other than environment. Yet, we do find a clear correlation between $U-V$ color and $r_{e}$ in low-mass red cluster galaxies ($\\log M_*/M_\\odot<9.8$) such that bluer systems are larger, with the bluest having sizes...

  17. Dhurrin metabolism in the developing grain of Sorghum bicolor (L.) Moench investigated by metabolite profiling and novel clustering analyses of time-resolved transcriptomic data

    DEFF Research Database (Denmark)

    Nielsen, Lasse Janniche; Stuart, Peter; Pičmanová, Martina

    2016-01-01

    Background: The important cereal crop Sorghum bicolor (L.) Moench biosynthesize and accumulate the defensive compound dhurrin during development. Previous work has suggested multiple roles for the compound including a function as nitrogen storage/buffer. Crucial for this function is the endogenous...... turnover of dhurrin for which putative pathways have been suggested but not confirmed. Results: In this study, the biosynthesis and endogenous turnover of dhurrin in the developing sorghum grain was studied by metabolite profiling and time-resolved transcriptome analyses. Dhurrin was found to accumulate...... in dhurrin content in the course of grain maturationrepresents the operation of hitherto uncharacterized endogenous dhurrin turnover pathways. Evidence for theoperation of two such pathways was obtained by metabolite profiling and time-resolved transcriptome analysis. By combining cluster- and phylogenetic...

  18. Parameterization and Observability Analysis of Scalable Battery Clusters for Onboard Thermal Management Paramétrage et analyse d’observabilité de clusters de batteries de taille variable pour une gestion thermique embarquée

    Directory of Open Access Journals (Sweden)

    Lin Xinfan

    2013-03-01

    paramétrage en ligne et un observateur adaptatif sont conçus pour une batterie cylindrique. Le modèle thermique à une seule cellule est ensuite agrandi afin de créer un modèle de cluster de batteries dans le but d’étudier le schéma de température du cluster. Les interconnexions thermiques modélisées entre les cellules incluent la conduction de chaleur de cellule à cellule et la convection au flux du liquide de refroidissement environnant. Une analyse d’observabilité est effectuée sur le cluster avant la conception, pour le pack, d’un observateur en boucle fermée. Sur la base de l’analyse, les lignes directrices permettant la détermination du nombre minimal de sondes requises et leurs positionnements exacts sont déduites permettant d’assurer l’observabilité de tous les états thermiques.

  19. Data Clustering

    Science.gov (United States)

    Wagstaff, Kiri L.

    2012-03-01

    clustering, in which some partial information about item assignments or other components of the resulting output are already known and must be accommodated by the solution. Some algorithms seek a partition of the data set into distinct clusters, while others build a hierarchy of nested clusters that can capture taxonomic relationships. Some produce a single optimal solution, while others construct a probabilistic model of cluster membership. More formally, clustering algorithms operate on a data set X composed of items represented by one or more features (dimensions). These could include physical location, such as right ascension and declination, as well as other properties such as brightness, color, temporal change, size, texture, and so on. Let D be the number of dimensions used to represent each item, xi ∈ RD. The clustering goal is to produce an organization P of the items in X that optimizes an objective function f : P -> R, which quantifies the quality of solution P. Often f is defined so as to maximize similarity within a cluster and minimize similarity between clusters. To that end, many algorithms make use of a measure d : X x X -> R of the distance between two items. A partitioning algorithm produces a set of clusters P = {c1, . . . , ck} such that the clusters are nonoverlapping (c_i intersected with c_j = empty set, i != j) subsets of the data set (Union_i c_i=X). Hierarchical algorithms produce a series of partitions P = {p1, . . . , pn }. For a complete hierarchy, the number of partitions n’= n, the number of items in the data set; the top partition is a single cluster containing all items, and the bottom partition contains n clusters, each containing a single item. For model-based clustering, each cluster c_j is represented by a model m_j , such as the cluster center or a Gaussian distribution. The wide array of available clustering algorithms may seem bewildering, and covering all of them is beyond the scope of this chapter. Choosing among them for a

  20. Automated tetraploid genotype calling by hierarchical clustering

    Science.gov (United States)

    SNP arrays are transforming breeding and genetics research for autotetraploids. To fully utilize these arrays, however, the relationship between signal intensity and allele dosage must be inferred independently for each marker. We developed an improved computational method to automate this process, ...

  1. Fuzzy clustering of physicochemical and biochemical properties of amino acids.

    Science.gov (United States)

    Saha, Indrajit; Maulik, Ujjwal; Bandyopadhyay, Sanghamitra; Plewczynski, Dariusz

    2012-08-01

    In this article, we categorize presently available experimental and theoretical knowledge of various physicochemical and biochemical features of amino acids, as collected in the AAindex database of known 544 amino acid (AA) indices. Previously reported 402 indices were categorized into six groups using hierarchical clustering technique and 142 were left unclustered. However, due to the increasing diversity of the database these indices are overlapping, therefore crisp clustering method may not provide optimal results. Moreover, in various large-scale bioinformatics analyses of whole proteomes, the proper selection of amino acid indices representing their biological significance is crucial for efficient and error-prone encoding of the short functional sequence motifs. In most cases, researchers perform exhaustive manual selection of the most informative indices. These two facts motivated us to analyse the widely used AA indices. The main goal of this article is twofold. First, we present a novel method of partitioning the bioinformatics data using consensus fuzzy clustering, where the recently proposed fuzzy clustering techniques are exploited. Second, we prepare three high quality subsets of all available indices. Superiority of the consensus fuzzy clustering method is demonstrated quantitatively, visually and statistically by comparing it with the previously proposed hierarchical clustered results. The processed AAindex1 database, supplementary material and the software are available at http://sysbio.icm.edu.pl/aaindex/ .

  2. Relationship between damage clustering and mortality in systemic lupus erythematosus in early and late stages of the disease: cluster analyses in a large cohort from the Spanish Society of Rheumatology Lupus Registry.

    Science.gov (United States)

    Pego-Reigosa, José María; Lois-Iglesias, Ana; Rúa-Figueroa, Íñigo; Galindo, María; Calvo-Alén, Jaime; de Uña-Álvarez, Jacobo; Balboa-Barreiro, Vanessa; Ibáñez Ruan, Jesús; Olivé, Alejandro; Rodríguez-Gómez, Manuel; Fernández Nebro, Antonio; Andrés, Mariano; Erausquin, Celia; Tomero, Eva; Horcada Rubio, Loreto; Uriarte Isacelaya, Esther; Freire, Mercedes; Montilla, Carlos; Sánchez-Atrio, Ana I; Santos-Soler, Gregorio; Zea, Antonio; Díez, Elvira; Narváez, Javier; Blanco-Alonso, Ricardo; Silva-Fernández, Lucía; Ruiz-Lucea, María Esther; Fernández-Castro, Mónica; Hernández-Beriain, José Ángel; Gantes-Mora, Marian; Hernández-Cruz, Blanca; Pérez-Venegas, José; Pecondón-Español, Ángela; Marras Fernández-Cid, Carlos; Ibáñez-Barcelo, Mónica; Bonilla, Gema; Torrente-Segarra, Vicenç; Castellví, Iván; Alegre, Juan José; Calvet, Joan; Marenco de la Fuente, José Luis; Raya, Enrique; Vázquez-Rodríguez, Tomás Ramón; Quevedo-Vila, Víctor; Muñoz-Fernández, Santiago; Otón, Teresa; Rahman, Anisur; López-Longo, Francisco Javier

    2016-07-01

    To identify patterns (clusters) of damage manifestations within a large cohort of SLE patients and evaluate the potential association of these clusters with a higher risk of mortality. This is a multicentre, descriptive, cross-sectional study of a cohort of 3656 SLE patients from the Spanish Society of Rheumatology Lupus Registry. Organ damage was ascertained using the Systemic Lupus International Collaborating Clinics Damage Index. Using cluster analysis, groups of patients with similar patterns of damage manifestations were identified. Then, overall clusters were compared as well as the subgroup of patients within every cluster with disease duration shorter than 5 years. Three damage clusters were identified. Cluster 1 (80.6% of patients) presented a lower amount of individuals with damage (23.2 vs 100% in clusters 2 and 3, P Cluster 2 (11.4% of patients) was characterized by musculoskeletal damage in all patients. Cluster 3 (8.0% of patients) was the only group with cardiovascular damage, and this was present in all patients. The overall mortality rate of patients in clusters 2 and 3 was higher than that in cluster 1 (P clusters. Both in early and late stages of the disease, there was a significant association of these clusters with an increased risk of mortality. Physicians should pay special attention to the early prevention of damage in these two systems. © The Author 2016. Published by Oxford University Press on behalf of the British Society for Rheumatology. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  3. Analysis hierarchical model for discrete event systems

    Science.gov (United States)

    Ciortea, E. M.

    2015-11-01

    The This paper presents the hierarchical model based on discrete event network for robotic systems. Based on the hierarchical approach, Petri network is analysed as a network of the highest conceptual level and the lowest level of local control. For modelling and control of complex robotic systems using extended Petri nets. Such a system is structured, controlled and analysed in this paper by using Visual Object Net ++ package that is relatively simple and easy to use, and the results are shown as representations easy to interpret. The hierarchical structure of the robotic system is implemented on computers analysed using specialized programs. Implementation of hierarchical model discrete event systems, as a real-time operating system on a computer network connected via a serial bus is possible, where each computer is dedicated to local and Petri model of a subsystem global robotic system. Since Petri models are simplified to apply general computers, analysis, modelling, complex manufacturing systems control can be achieved using Petri nets. Discrete event systems is a pragmatic tool for modelling industrial systems. For system modelling using Petri nets because we have our system where discrete event. To highlight the auxiliary time Petri model using transport stream divided into hierarchical levels and sections are analysed successively. Proposed robotic system simulation using timed Petri, offers the opportunity to view the robotic time. Application of goods or robotic and transmission times obtained by measuring spot is obtained graphics showing the average time for transport activity, using the parameters sets of finished products. individually.

  4. 基于主成分与聚类分析的苹果加工品质评价%Evaluation of apple quality based on principal component and hierarchical cluster analysis

    Institute of Scientific and Technical Information of China (English)

    公丽艳; 孟宪军; 刘乃侨; 毕金峰

    2014-01-01

    The purpose of this study was to investigate the variations in physical and chemical characteristics of apple fruit from 30 varieties grown in the same place using pattern recognition tools. Twenty quality parameters of apple samples (e.g. weight,volume, density, color, hardness, sugar-acid ratio, Vitamin C, etc.) were analyzed. Interrelationships between the parameters and the apple variety were investigated by descriptive statistics, principal component analysis (PCA) and hierarchical cluster analysis (HCA). PCA is a mathematical tool which performs a reduction in data dimensionality and allows the visualisation of underlying structure in experimental data and relationships between data and samples.In hierarchical cluster analysis, samples are grouped on the basis of similarities, without taking into account the information about the class membership. The results obtained following HCA are shown as a dendrogram in which five well-defined clusters are visible. Samples will be grouped in clusters in terms of their nearness or similarity. Cluster analysis uses less information (distances only) than PCA. It is interesting to observe what kind of classification can be made on the basis of distances only. The results showed that density, fruit shape index and water content of 30 apple varieties were not significantly different. The remaining seventeen measurements were investigated by principal component analysis. The first six components represented 83.56% of the total variability on the base of the total variance explained and screen plot of principal component analysis. The first principal component was related to titratable acidity, sugar-acid ratio and solid-acid ratio attributes, which were the taste quality factor. The second principal component was related to L,a, andb attributes, which were the color factor. Following that were sweetness factor, texture factor, quality factor and size factor. The sample score plots visually displayed the relationship between

  5. Comparative Study of K-means and Robust Clustering

    Directory of Open Access Journals (Sweden)

    Shashi Sharma

    2013-09-01

    Full Text Available Data mining is the mechanism of implementing patterns in large amount of data sets involving methods at the intersection of artificial intelligence, machine learning, statistics, and database systems. Clustering is the very big area in which grouping of same type of objects in data mining. Clustering has divided into different categories – partitioned clustering and hierarchical clustering. In this paper we study two types of clustering first is Kmeans which is part of partitioned clustering. Kmeans clustering generates a specific number of disjoint, flat (non-hierarchical clusters. Second clustering is robust clustering which is part of hierarchical clustering. This clustering uses Jaccard coefficient instead of using the distance measures to find the similarity between the data or documents to classify the clusters. We show comparison between Kmeans clustering and robust clustering which is better for categorical data.

  6. Non-Trivial Feature Derivation for Intensifying Feature Detection Using LIDAR Datasets Through Allometric Aggregation Data Analysis Applying Diffused Hierarchical Clustering for Discriminating Agricultural Land Cover in Portions of Northern Mindanao, Philippines

    Science.gov (United States)

    Villar, Ricardo G.; Pelayo, Jigg L.; Mozo, Ray Mari N.; Salig, James B., Jr.; Bantugan, Jojemar

    2016-06-01

    Leaning on the derived results conducted by Central Mindanao University Phil-LiDAR 2.B.11 Image Processing Component, the paper attempts to provides the application of the Light Detection and Ranging (LiDAR) derived products in arriving quality Landcover classification considering the theoretical approach of data analysis principles to minimize the common problems in image classification. These are misclassification of objects and the non-distinguishable interpretation of pixelated features that results to confusion of class objects due to their closely-related spectral resemblance, unbalance saturation of RGB information is a challenged at the same time. Only low density LiDAR point cloud data is exploited in the research denotes as 2 pts/m2 of accuracy which bring forth essential derived information such as textures and matrices (number of returns, intensity textures, nDSM, etc.) in the intention of pursuing the conditions for selection characteristic. A novel approach that takes gain of the idea of object-based image analysis and the principle of allometric relation of two or more observables which are aggregated for each acquisition of datasets for establishing a proportionality function for data-partioning. In separating two or more data sets in distinct regions in a feature space of distributions, non-trivial computations for fitting distribution were employed to formulate the ideal hyperplane. Achieving the distribution computations, allometric relations were evaluated and match with the necessary rotation, scaling and transformation techniques to find applicable border conditions. Thus, a customized hybrid feature was developed and embedded in every object class feature to be used as classifier with employed hierarchical clustering strategy for cross-examining and filtering features. This features are boost using machine learning algorithms as trainable sets of information for a more competent feature detection. The product classification in this

  7. Multi-granularity reconstruction of 3D calamity emergency situations based on visual scale space hierarchical clustering%基于VSSHC算法的灾害应急多粒度三维态势重构

    Institute of Scientific and Technical Information of China (English)

    于海心; 陈杰; 张娟

    2012-01-01

    针对现有灾害应急态势系统的三维地貌实时更新和态势多粒度显示的技术瓶颈,研究并设计了应急三维态势重构系统(3D-ESRS),该系统可进行实时三维地貌更新和多粒度显示态势内容.分析了3D-ESRS的需求和功能,设计了3D-ESRS的基于多智能体(MAS)的系统框架结构,研究了3D-ESRS系统更新地貌和多粒度显示原理与工作流程,构建了基于视觉尺度空间分层聚类(VSSHC)算法的多尺度分类模型.以堰塞湖为例,多粒度显示了水面升高过程,该实验结果表明3D-ESRS与传统基于GIS平台的态势系统相比,可以实时进行三维地貌场景更新,并对场景进行多粒度显示.%A calamity-oriented 3D emergency situation reconstruction system (3D-ESRS) was studied, and its architecture was designed using the multi-agent technique.Moreover, an approach to multi-granularity reconstruction of 3D calamity emergency situations based on the visual scale space hierarchical clustering ( VSSHC) algorithm was proposed for calamity emergency-decision supporting systems to make them realize the real-time presentation of dynamic 3D calamity situations.A simulation platform based on high level architecture (HLA) was established to verify this approach.The simulation results illustrate that this approach is applicable to emergency-decision supporting systems, and compared to the traditional situation display system this 3D-ESRS has the superiority in reconstructing real-time 3D scenario models.

  8. 一种基于分层 AP 的视频关键帧提取方法研究%Research on video key-frame extraction based on hierarchical affinity propagation clustering

    Institute of Scientific and Technical Information of China (English)

    党宏社; 白梅

    2016-01-01

    为从大量的视频资源中高效准确地提取关键帧图像来表达视频的主要内容,针对传统AP聚类方法提取关键帧无法适应大规模图像集的问题,提出一种分层AP的关键帧提取方法。提取所有视频序列的颜色和纹理特征,将待聚类的图像集进行分层,用传统AP聚类方法求取每个图像子集的聚类中心;用得到的聚类中心进行自适应的AP聚类,根据Silhouette指标选取最优的聚类结果,即可得到视频序列的关键帧代表。实验表明,该方法能快速准确地提取视频最优关键帧,在保证保真度指标的同时能提高关键帧提取的压缩比,且适用于不同类型的视频资源。%In order to extract key frames from large‐scale different videos more effectively and accurately ,since traditional AP algorithm is inappropriate to the large‐scale pictures cluste‐ring ,a hierarchical AP method for key frame extraction is proposed .First get the color and texture features of all video sequences ,the pictures set is divided into several subsets ,the tra‐ditional AP is used to obtain the exemplars of each subset ;Then the adaptive AP is imple‐mented on the obtained exemplars ,the key frames of video sequences are extracted according to the index of Silhouette for the best clustering result .The experimental result shows that proposed method is efficient in key‐frame extraction and suitable for all types video re‐sources ,has a high fidelity w hile the compression ratio is improved greatly .

  9. The Nature and Nurture of Star Clusters

    CERN Document Server

    Elmegreen, Bruce G

    2009-01-01

    Star clusters have hierarchical patterns in space and time, suggesting formation processes in the densest regions of a turbulent interstellar medium. Clusters also have hierarchical substructure when they are young, which makes them all look like the inner mixed parts of a pervasive stellar hierarchy. Young field stars share this distribution, presumably because some of them came from dissolved clusters and others formed in a dispersed fashion in the same gas. The fraction of star formation that ends up in clusters is apparently not constant, but may increase with interstellar pressure. Hierarchical structure explains why stars form in clusters and why many of these clusters are self-bound. It also explains the cluster mass function. Halo globular clusters share many properties of disk clusters, including what appears to be an upper cluster cutoff mass. However, halo globulars are self-enriched and often connected with dwarf galaxy streams. The mass function of halo globulars could have initially been like th...

  10. A hierarchical linear model for tree height prediction.

    Science.gov (United States)

    Vicente J. Monleon

    2003-01-01

    Measuring tree height is a time-consuming process. Often, tree diameter is measured and height is estimated from a published regression model. Trees used to develop these models are clustered into stands, but this structure is ignored and independence is assumed. In this study, hierarchical linear models that account explicitly for the clustered structure of the data...

  11. Investigation on IMCP based clustering in LTE-M communication for smart metering applications

    National Research Council Canada - National Science Library

    Kartik Vishal Deshpande; A. Rajesh

    2017-01-01

    .... This paper investigates the proposed Improved M2M Clustering Process (IMCP) based clustering technique and it is compared with two well-known clustering algorithms, namely, Low Energy Adaptive Clustering Hierarchical (LEACH...

  12. Frontoparietal Connectivity and Hierarchical Structure of the Brain’s Functional Network during Sleep

    Directory of Open Access Journals (Sweden)

    Victor I Spoormaker

    2012-05-01

    Full Text Available Frontal and parietal regions are associated with some of the most complex cognitive functions, and several frontoparietal resting-state networks can be observed in wakefulness. We used functional magnetic resonance imaging (fMRI data acquired in polysomnographically validated wakefulness, light sleep and slow-wave sleep to examine the hierarchical structure of a low-frequency functional brain network, and to examine whether frontoparietal connectivity would disintegrate in sleep. Whole-brain analyses with hierarchical cluster analysis on predefined atlases were performed, as well as regression of inferior parietal lobules seeds against all voxels in the brain, and an evaluation of the integrity of voxel time-courses in subcortical regions-of-interest. We observed that frontoparietal functional connectivity disintegrated in sleep stage 1 and was absent in deeper sleep stages. Slow-wave sleep was characterized by strong hierarchical clustering of local submodules. Frontoparietal connectivity between inferior parietal lobules and superior medial and right frontal gyrus was lower in sleep stages than in wakefulness. Moreover, thalamus voxels showed maintained integrity in sleep stage 1, making intrathalamic desynchronization an unlikely source of reduced thalamocortical connectivity in this sleep stage. Our data suggest a transition from a globally integrated functional brain network in wakefulness to a disintegrated network consisting of local submodules in slow-wave sleep, in which frontoparietal inter-modular nodes may play a crucial role, possibly in combination with the thalamus.

  13. Investigation of major international and Turkish companies via hierarchical methods and bootstrap approach

    Science.gov (United States)

    Kantar, E.; Deviren, B.; Keskin, M.

    2011-11-01

    We present a study, within the scope of econophysics, of the hierarchical structure of 98 among the largest international companies including 18 among the largest Turkish companies, namely Banks, Automobile, Software-hardware, Telecommunication Services, Energy and the Oil-Gas sectors, viewed as a network of interacting companies. We analyze the daily time series data of the Boerse-Frankfurt and Istanbul Stock Exchange. We examine the topological properties among the companies over the period 2006-2010 by using the concept of hierarchical structure methods (the minimal spanning tree (MST) and the hierarchical tree (HT)). The period is divided into three subperiods, namely 2006-2007, 2008 which was the year of global economic crisis, and 2009-2010, in order to test various time-windows and observe temporal evolution. We carry out bootstrap analyses to associate the value of statistical reliability to the links of the MSTs and HTs. We also use average linkage clustering analysis (ALCA) in order to better observe the cluster structure. From these studies, we find that the interactions among the Banks/Energy sectors and the other sectors were reduced after the global economic crisis; hence the effects of the Banks and Energy sectors on the correlations of all companies were decreased. Telecommunication Services were also greatly affected by the crisis. We also observed that the Automobile and Banks sectors, including Turkish companies as well as some companies from the USA, Japan and Germany were strongly correlated with each other in all periods.

  14. Hierarchical models and chaotic spin glasses

    Science.gov (United States)

    Berker, A. Nihat; McKay, Susan R.

    1984-09-01

    Renormalization-group studies in position space have led to the discovery of hierarchical models which are exactly solvable, exhibiting nonclassical critical behavior at finite temperature. Position-space renormalization-group approximations that had been widely and successfully used are in fact alternatively applicable as exact solutions of hierarchical models, this realizability guaranteeing important physical requirements. For example, a hierarchized version of the Sierpiriski gasket is presented, corresponding to a renormalization-group approximation which has quantitatively yielded the multicritical phase diagrams of submonolayers on graphite. Hierarchical models are now being studied directly as a testing ground for new concepts. For example, with the introduction of frustration, chaotic renormalization-group trajectories were obtained for the first time. Thus, strong and weak correlations are randomly intermingled at successive length scales, and a new microscopic picture and mechanism for a spin glass emerges. An upper critical dimension occurs via a boundary crisis mechanism in cluster-hierarchical variants developed to have well-behaved susceptibilities.

  15. Hierarchical auxetic mechanical metamaterials.

    Science.gov (United States)

    Gatt, Ruben; Mizzi, Luke; Azzopardi, Joseph I; Azzopardi, Keith M; Attard, Daphne; Casha, Aaron; Briffa, Joseph; Grima, Joseph N

    2015-02-11

    Auxetic mechanical metamaterials are engineered systems that exhibit the unusual macroscopic property of a negative Poisson's ratio due to sub-unit structure rather than chemical composition. Although their unique behaviour makes them superior to conventional materials in many practical applications, they are limited in availability. Here, we propose a new class of hierarchical auxetics based on the rotating rigid units mechanism. These systems retain the enhanced properties from having a negative Poisson's ratio with the added benefits of being a hierarchical system. Using simulations on typical hierarchical multi-level rotating squares, we show that, through design, one can control the extent of auxeticity, degree of aperture and size of the different pores in the system. This makes the system more versatile than similar non-hierarchical ones, making them promising candidates for industrial and biomedical applications, such as stents and skin grafts.

  16. Introduction into Hierarchical Matrices

    KAUST Repository

    Litvinenko, Alexander

    2013-12-05

    Hierarchical matrices allow us to reduce computational storage and cost from cubic to almost linear. This technique can be applied for solving PDEs, integral equations, matrix equations and approximation of large covariance and precision matrices.

  17. Hierarchical Auxetic Mechanical Metamaterials

    Science.gov (United States)

    Gatt, Ruben; Mizzi, Luke; Azzopardi, Joseph I.; Azzopardi, Keith M.; Attard, Daphne; Casha, Aaron; Briffa, Joseph; Grima, Joseph N.

    2015-02-01

    Auxetic mechanical metamaterials are engineered systems that exhibit the unusual macroscopic property of a negative Poisson's ratio due to sub-unit structure rather than chemical composition. Although their unique behaviour makes them superior to conventional materials in many practical applications, they are limited in availability. Here, we propose a new class of hierarchical auxetics based on the rotating rigid units mechanism. These systems retain the enhanced properties from having a negative Poisson's ratio with the added benefits of being a hierarchical system. Using simulations on typical hierarchical multi-level rotating squares, we show that, through design, one can control the extent of auxeticity, degree of aperture and size of the different pores in the system. This makes the system more versatile than similar non-hierarchical ones, making them promising candidates for industrial and biomedical applications, such as stents and skin grafts.

  18. Applied Bayesian Hierarchical Methods

    CERN Document Server

    Congdon, Peter D

    2010-01-01

    Bayesian methods facilitate the analysis of complex models and data structures. Emphasizing data applications, alternative modeling specifications, and computer implementation, this book provides a practical overview of methods for Bayesian analysis of hierarchical models.

  19. Programming with Hierarchical Maps

    DEFF Research Database (Denmark)

    Ørbæk, Peter

    This report desribes the hierarchical maps used as a central data structure in the Corundum framework. We describe its most prominent features, ague for its usefulness and briefly describe some of the software prototypes implemented using the technology....

  20. Catalysis with hierarchical zeolites

    DEFF Research Database (Denmark)

    Holm, Martin Spangsberg; Taarning, Esben; Egeblad, Kresten

    2011-01-01

    Hierarchical (or mesoporous) zeolites have attracted significant attention during the first decade of the 21st century, and so far this interest continues to increase. There have already been several reviews giving detailed accounts of the developments emphasizing different aspects of this research...... topic. Until now, the main reason for developing hierarchical zeolites has been to achieve heterogeneous catalysts with improved performance but this particular facet has not yet been reviewed in detail. Thus, the present paper summaries and categorizes the catalytic studies utilizing hierarchical...... zeolites that have been reported hitherto. Prototypical examples from some of the different categories of catalytic reactions that have been studied using hierarchical zeolite catalysts are highlighted. This clearly illustrates the different ways that improved performance can be achieved with this family...

  1. Dynamic Organization of Hierarchical Memories.

    Science.gov (United States)

    Kurikawa, Tomoki; Kaneko, Kunihiko

    2016-01-01

    In the brain, external objects are categorized in a hierarchical way. Although it is widely accepted that objects are represented as static attractors in neural state space, this view does not take account interaction between intrinsic neural dynamics and external input, which is essential to understand how neural system responds to inputs. Indeed, structured spontaneous neural activity without external inputs is known to exist, and its relationship with evoked activities is discussed. Then, how categorical representation is embedded into the spontaneous and evoked activities has to be uncovered. To address this question, we studied bifurcation process with increasing input after hierarchically clustered associative memories are learned. We found a "dynamic categorization"; neural activity without input wanders globally over the state space including all memories. Then with the increase of input strength, diffuse representation of higher category exhibits transitions to focused ones specific to each object. The hierarchy of memories is embedded in the transition probability from one memory to another during the spontaneous dynamics. With increased input strength, neural activity wanders over a narrower state space including a smaller set of memories, showing more specific category or memory corresponding to the applied input. Moreover, such coarse-to-fine transitions are also observed temporally during transient process under constant input, which agrees with experimental findings in the temporal cortex. These results suggest the hierarchy emerging through interaction with an external input underlies hierarchy during transient process, as well as in the spontaneous activity.

  2. A MULTIVARIATE APPROACH TO ANALYSE NATIVE FOREST TREE SPECIE SEEDS

    Directory of Open Access Journals (Sweden)

    Alessandro Dal Col Lúcio

    2006-03-01

    Full Text Available This work grouped, by species, the most similar seed tree, using the variables observed in exotic forest species of theBrazilian flora of seeds collected in the Forest Research and Soil Conservation Center of Santa Maria, Rio Grande do Sul, analyzedfrom January, 1997, to march, 2003. For the cluster analysis, all the species that possessed four or more analyses per lot wereanalyzed by the hierarchical Clustering method, of the standardized Euclidian medium distance, being also a principal componentanalysis technique for reducing the number of variables. The species Callistemon speciosus, Cassia fistula, Eucalyptus grandis,Eucalyptus robusta, Eucalyptus saligna, Eucalyptus tereticornis, Delonix regia, Jacaranda mimosaefolia e Pinus elliottii presentedmore than four analyses per lot, in which the third and fourth main components explained 80% of the total variation. The clusteranalysis was efficient in the separation of the groups of all tested species, as well as the method of the main components.

  3. [Cluster analysis in biomedical researches].

    Science.gov (United States)

    Akopov, A S; Moskovtsev, A A; Dolenko, S A; Savina, G D

    2013-01-01

    Cluster analysis is one of the most popular methods for the analysis of multi-parameter data. The cluster analysis reveals the internal structure of the data, group the separate observations on the degree of their similarity. The review provides a definition of the basic concepts of cluster analysis, and discusses the most popular clustering algorithms: k-means, hierarchical algorithms, Kohonen networks algorithms. Examples are the use of these algorithms in biomedical research.

  4. A hybrid clustering approach to recognition of protein families in 114 microbial genomes

    Directory of Open Access Journals (Sweden)

    Gogarten J Peter

    2004-04-01

    Full Text Available Abstract Background Grouping proteins into sequence-based clusters is a fundamental step in many bioinformatic analyses (e.g., homology-based prediction of structure or function. Standard clustering methods such as single-linkage clustering capture a history of cluster topologies as a function of threshold, but in practice their usefulness is limited because unrelated sequences join clusters before biologically meaningful families are fully constituted, e.g. as the result of matches to so-called promiscuous domains. Use of the Markov Cluster algorithm avoids this non-specificity, but does not preserve topological or threshold information about protein families. Results We describe a hybrid approach to sequence-based clustering of proteins that combines the advantages of standard and Markov clustering. We have implemented this hybrid approach over a relational database environment, and describe its application to clustering a large subset of PDB, and to 328577 proteins from 114 fully sequenced microbial genomes. To demonstrate utility with difficult problems, we show that hybrid clustering allows us to constitute the paralogous family of ATP synthase F1 rotary motor subunits into a single, biologically interpretable hierarchical grouping that was not accessible using either single-linkage or Markov clustering alone. We describe validation of this method by hybrid clustering of PDB and mapping SCOP families and domains onto the resulting clusters. Conclusion Hybrid (Markov followed by single-linkage clustering combines the advantages of the Markov Cluster algorithm (avoidance of non-specific clusters resulting from matches to promiscuous domains and single-linkage clustering (preservation of topological information as a function of threshold. Within the individual Markov clusters, single-linkage clustering is a more-precise instrument, discerning sub-clusters of biological relevance. Our hybrid approach thus provides a computationally efficient

  5. The ages of young star clusters, massive blue stragglers and the upper mass limit of stars: analysing age dependent stellar mass functions

    CERN Document Server

    Schneider, Fabian R N; de Mink, Selma E; Langer, Norbert; Stolte, Andrea; de Koter, Alex; Gvaramadze, Vasilii V; Hußmann, Benjamin; Liermann, Adriane; Sana, Hugues

    2013-01-01

    Massive stars rapidly change their masses through strong stellar winds and mass transfer in binary systems. We show that such mass changes leave characteristic signatures in stellar mass functions of young star clusters which can be used to infer their ages and to identify products of binary evolution. We model the observed present day mass functions of the young Galactic Arches and Quintuplet star clusters using our rapid binary evolution code. We find that shaping of the mass function by stellar wind mass loss allows us to determine the cluster ages to 3.5$\\pm$0.7 Myr and 4.8$\\pm$1.1 Myr, respectively. Exploiting the effects of binary mass exchange on the cluster mass function, we find that the most massive stars in both clusters are rejuvenated products of binary mass transfer, i.e. the massive counterpart of classical blue straggler stars. This resolves the problem of an apparent age spread among the most luminous stars exceeding the expected duration of star formation in these clusters. We perform Monte ...

  6. Hierarchical control of electron-transfer

    DEFF Research Database (Denmark)

    Westerhoff, Hans V.; Jensen, Peter Ruhdal; Egger, Louis;

    1997-01-01

    In this chapter the role of electron transfer in determining the behaviour of the ATP synthesising enzyme in E. coli is analysed. It is concluded that the latter enzyme lacks control because of special properties of the electron transfer components. These properties range from absence of a strong...... back pressure by the protonmotive force on the rate of electron transfer to hierarchical regulation of the expression of the gens that encode the electron transfer proteins as a response to changes in the bioenergetic properties of the cell.The discussion uses Hierarchical Control Analysis...

  7. The association between content of the elements S, Cl, K, Fe, Cu, Zn and Br in normal and cirrhotic liver tissue from Danes and Greenlandic Inuit examined by dual hierarchical clustering analysis

    DEFF Research Database (Denmark)

    Laursen, Jens; Milman, Nils; Pind, N.;

    2014-01-01

    contents according to calculated similarities, one clustering elements according to correlation coefficients between the element contents, both using Euclidian distance and Ward Procedure. RESULTS: One dendrogram separated subjects in 7 clusters showing no differences in ethnicity, gender or age....... The analysis discriminated between elements in normal and cirrhotic livers. The other dendrogram clustered elements in four clusters: sulphur and chlorine; copper and bromine; potassium and zinc; iron. There were significant correlations between the elements in normal liver samples: S was associated with Cl, K...

  8. Parallel hierarchical radiosity rendering

    Energy Technology Data Exchange (ETDEWEB)

    Carter, M.

    1993-07-01

    In this dissertation, the step-by-step development of a scalable parallel hierarchical radiosity renderer is documented. First, a new look is taken at the traditional radiosity equation, and a new form is presented in which the matrix of linear system coefficients is transformed into a symmetric matrix, thereby simplifying the problem and enabling a new solution technique to be applied. Next, the state-of-the-art hierarchical radiosity methods are examined for their suitability to parallel implementation, and scalability. Significant enhancements are also discovered which both improve their theoretical foundations and improve the images they generate. The resultant hierarchical radiosity algorithm is then examined for sources of parallelism, and for an architectural mapping. Several architectural mappings are discussed. A few key algorithmic changes are suggested during the process of making the algorithm parallel. Next, the performance, efficiency, and scalability of the algorithm are analyzed. The dissertation closes with a discussion of several ideas which have the potential to further enhance the hierarchical radiosity method, or provide an entirely new forum for the application of hierarchical methods.

  9. Clustering Approach to Stock Market Prediction

    Directory of Open Access Journals (Sweden)

    M.Suresh Babu

    2012-01-01

    Full Text Available Clustering is an adaptive procedure in which objects are clustered or grouped together, based on the principle of maximizing the intra-class similarity and minimizing the inter-class similarity. Various clustering algorithms have been developed which results to a good performance on datasets for cluster formation. This paper analyze the major clustering algorithms: K-Means, Hierarchical clustering algorithm and reverse K means and compare the performance of these three major clustering algorithms on the aspect of correctly class wise cluster building ability of algorithm. An effective clustering method, HRK (Hierarchical agglomerative and Recursive K-means clustering is proposed, to predict the short-term stock price movements after the release of financial reports. The proposed method consists of three phases. First, we convert each financial report into a feature vector and use the hierarchical agglomerative clustering method to divide the converted feature vectors into clusters. Second, for each cluster, we recursively apply the K-means clustering method to partition each cluster into sub-clusters so that most feature vectors in each subcluster belong to the same class. Then, for each sub cluster, we choose its centroid as the representative feature vector. Finally, we employ the representative feature vectors to predict the stock price movements. The experimental results show the proposed method outperforms SVM in terms of accuracy and average profits.

  10. Two genetic clusters in swine hemoplasmas revealed by analyses of the 16S rRNA and RNase P RNA genes.

    Science.gov (United States)

    Watanabe, Yusaku; Fujihara, Masatoshi; Obara, Hisato; Nagai, Kazuya; Harasawa, Ryô

    2011-12-01

    Only two hemoplasma species, Eperythrozoon parvum and Mycoplasma suis, have been recognized in pigs. Here we demonstrate the genetic variations among six hemoplasma strains detected from pigs, by analyzing the 16S rRNA and RNase P RNA (rnpB) genes, and propose a novel hemoplasma taxon that has not been described previously. Phylogenetic trees based on the nucleotide sequence of the 16S rRNA gene indicated that these six hemoplasmas were divided into two clusters representing M. suis and a novel taxon. We further examined the primary and secondary structures of the nucleotide sequences of the rnpB gene of the novel taxon, and found it distinct from that of M. suis. In conclusion, we unveiled a genetic cluster distinct from M. suis, suggesting a new swine hemoplasma species or E. parvum. Our findings also suggest that this novel cluster should be included in the genus Mycoplasma.

  11. Wind farms model aggregation using probabilistic clustering

    Science.gov (United States)

    Fernandes, Paula Odete; Ferreira, Ángela Paula

    2013-10-01

    The main objective of this research is the identification of homogeneous groups within wind farms of a major operator playing in the energy sector in Portugal, based on two multivariate analyses: Hierarchical Cluster Analysis and Discriminant Analysis, by using two independent variables: annual liquid hours and net production. From the produced outputs there were identified three homogenous groups of wind farms: (1) medium Installed Capacity and Induction Generator based Technology, (2) high Installed Capacity and Synchronous Generator based Technology and (3) medium Installed Capacity and Synchronous Generator based Technology, which includes the wind farms with the higher annual liquid hours. It has been found that the results obtained by cluster analysis are well classified, with a total percentage of correct classification of 97,1%, which can be considered excellent.

  12. The Grism Lens-amplified Survey from Space (Glass). IX. The Dual Origin of Low-mass Cluster Galaxies as Revealed by New Structural Analyses

    Science.gov (United States)

    Morishita, Takahiro; Abramson, Louis E.; Treu, Tommaso; Vulcani, Benedetta; Schmidt, Kasper B.; Dressler, Alan; Poggianti, Bianca M.; Malkan, Matthew A.; Wang, Xin; Huang, Kuang-Han; Trenti, Michele; Bradač, Maruša; Hoag, Austin

    2017-02-01

    Using deep Hubble Frontier Fields imaging and slitless spectroscopy from the Grism Survey from Space, we study 2200 cluster and 1748 field galaxies at 0.2≤slant z≤slant 0.7 to determine the impact of environment on galaxy size and structure at stellar masses {log}{M}* /{M}ȯ > 7.8, an unprecedented limit at these redshifts. Based on simple assumptions—{r}e=f({M}* )—we find no significant differences in half-light radii (r e ) between equal-mass cluster or field systems. More complex analyses—{r}e=f({M}* ,U-V,n,z,{{Σ }})—reveal local density (Σ) to induce only a 7% ± 3% (95% confidence) reduction in r e beyond what can be accounted for by U ‑ V color, Sérsic index (n), and redshift (z) effects. Almost any size difference between galaxies in high- and low-density regions is thus attributable to their different distributions in properties other than environment. Indeed, we find a clear color–r e correlation in low-mass passive cluster galaxies ({log}{M}* /{M}ȯ star-forming galaxies. We take this as evidence that large-r e low-mass passive cluster galaxies are recently acquired systems that have been environmentally quenched without significant structural transformation (e.g., by ram pressure stripping or starvation). Conversely, ∼20% of small-r e low-mass passive cluster galaxies appear to have been in place since z≳ 3. Given the consistency of the small-r e galaxies’ stellar surface densities (and even colors) with those of systems more than ten times as massive, our findings suggest that clusters mark places where galaxy evolution is accelerated for an ancient base population spanning most masses, with late-time additions quenched by environment-specific mechanisms mainly restricted to the lowest masses.

  13. Strategic games on a hierarchical network model

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    Among complex network models, the hierarchical network model is the one most close to such real networks as world trade web, metabolic network, WWW, actor network, and so on. It has not only the property of power-law degree distribution, but growth based on growth and preferential attachment, showing the scale-free degree distribution property. In this paper, we study the evolution of cooperation on a hierarchical network model, adopting the prisoner's dilemma (PD) game and snowdrift game (SG) as metaphors of the interplay between connected nodes. BA model provides a unifying framework for the emergence of cooperation. But interestingly, we found that on hierarchical model, there is no sign of cooperation for PD game, while the frequency of cooperation decreases as the common benefit decreases for SG. By comparing the scaling clustering coefficient properties of the hierarchical network model with that of BA model, we found that the former amplifies the effect of hubs. Considering different performances of PD game and SG on complex network, we also found that common benefit leads to cooperation in the evolution. Thus our study may shed light on the emergence of cooperation in both natural and social environments.

  14. Clustering of resting state networks.

    Directory of Open Access Journals (Sweden)

    Megan H Lee

    Full Text Available BACKGROUND: The goal of the study was to demonstrate a hierarchical structure of resting state activity in the healthy brain using a data-driven clustering algorithm. METHODOLOGY/PRINCIPAL FINDINGS: The fuzzy-c-means clustering algorithm was applied to resting state fMRI data in cortical and subcortical gray matter from two groups acquired separately, one of 17 healthy individuals and the second of 21 healthy individuals. Different numbers of clusters and different starting conditions were used. A cluster dispersion measure determined the optimal numbers of clusters. An inner product metric provided a measure of similarity between different clusters. The two cluster result found the task-negative and task-positive systems. The cluster dispersion measure was minimized with seven and eleven clusters. Each of the clusters in the seven and eleven cluster result was associated with either the task-negative or task-positive system. Applying the algorithm to find seven clusters recovered previously described resting state networks, including the default mode network, frontoparietal control network, ventral and dorsal attention networks, somatomotor, visual, and language networks. The language and ventral attention networks had significant subcortical involvement. This parcellation was consistently found in a large majority of algorithm runs under different conditions and was robust to different methods of initialization. CONCLUSIONS/SIGNIFICANCE: The clustering of resting state activity using different optimal numbers of clusters identified resting state networks comparable to previously obtained results. This work reinforces the observation that resting state networks are hierarchically organized.

  15. Comparative Genomic Analyses of Multiple Pseudomonas Strains Infecting Corylus avellana Trees Reveal the Occurrence of Two Genetic Clusters with Both Common and Distinctive Virulence and Fitness Traits.

    Directory of Open Access Journals (Sweden)

    Simone Marcelletti

    Full Text Available The European hazelnut (Corylus avellana is threatened in Europe by several pseudomonads which cause symptoms ranging from twig dieback to tree death. A comparison of the draft genomes of nine Pseudomonas strains isolated from symptomatic C. avellana trees was performed to identify common and distinctive genomic traits. The thorough assessment of genetic relationships among the strains revealed two clearly distinct clusters: P. avellanae and P. syringae. The latter including the pathovars avellanae, coryli and syringae. Between these two clusters, no recombination event was found. A genomic island of approximately 20 kb, containing the hrp/hrc type III secretion system gene cluster, was found to be present without any genomic difference in all nine pseudomonads. The type III secretion system effector repertoires were remarkably different in the two groups, with P. avellanae showing a higher number of effectors. Homologue genes of the antimetabolite mangotoxin and ice nucleation activity clusters were found solely in all P. syringae pathovar strains, whereas the siderophore yersiniabactin was only present in P. avellanae. All nine strains have genes coding for pectic enzymes and sucrose metabolism. By contrast, they do not have genes coding for indolacetic acid and anti-insect toxin. Collectively, this study reveals that genomically different Pseudomonas can converge on the same host plant by suppressing the host defence mechanisms with the use of different virulence weapons. The integration into their genomes of a horizontally acquired genomic island could play a fundamental role in their evolution, perhaps giving them the ability to exploit new ecological niches.

  16. Design and synthesis of "dumb-bell" and "triangular" inorganic-organic hybrid nanopolyoxometalate clusters and their characterisation through ESI-MS analyses.

    Science.gov (United States)

    Pradeep, Chullikkattil P; Li, Feng-Yan; Lydon, Claire; Miras, Haralampos N; Long, De-Liang; Xu, Lin; Cronin, Leroy

    2011-06-27

    A series of tris(hydroxymethyl)aminomethane (TRIS)-based linear (bis(TRIS)) and triangular (tris(TRIS)) ligands has been synthesised and were covalently attached to the Wells-Dawson type cluster [P(2)V(3)W(15)O(62)](9-) to generate a series of nanometer-sized inorganic-organic hybrid polyoxometalate clusters. These huge hybrids, with a molecular mass similar to that of small proteins in the range of ≈10-16 kDa, were unambiguously characterised by using high-resolution ESI-MS. The ESI-MS spectra of these compounds revealed, in negative ion mode, a characteristic pattern showing distinct groups of peaks corresponding to different anionic charge states ranging from 3(-) to 8(-) for the hybrids. Each peak in these individual groups could be unambiguously assigned to the corresponding hybrid cluster anion with varying combinations of tetrabutylammonium (TBA) and other cations. This study therefore highlights the prowess of the high-resolution ESI-MS for the unambiguous characterisation of large, nanoscale, inorganic-organic hybrid clusters that have huge mass, of the order of 10-16 kDa. Also, the designed synthesis of these compounds points to the fact that we were able to achieve a great deal of structural pre-design in the synthesis of these inorganic-organic hybrid polyoxometalates (POMs) by means of a ligand design route, which is often not possible in traditional "one-pot" POM synthesis.

  17. Hierarchical organisation of Britain through percolation theory

    CERN Document Server

    Arcaute, Elsa; Hatna, Erez; Murcio, Roberto; Vargas-Ruiz, Camilo; Masucci, Paolo; Wang, Jiaqiu; Batty, Michael

    2015-01-01

    Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations, which are the outcome of geographical, political and historical constraints. Using percolation theory on the street intersections and on the road network of Britain, we obtain hierarchies at different scales that are independent of administrative arrangements. Natural boundaries, such as islands and National Parks, consistently emerge at the largest/regional scales. Cities are devised through recursive percolations on each of the emerging clusters, but the system does not undergo a phase transition at the distance threshold at which cities can be defined. This specific distance is obtained by computing the fractal dimension of the clusters extracted at each distance threshold. We observe that the fractal dimension presents a maximum over all the different distance thresholds. The clusters obtained at this maximum are in very good correspondence to the morphological definition of...

  18. Hierarchical Porous Structures

    Energy Technology Data Exchange (ETDEWEB)

    Grote, Christopher John [Los Alamos National Lab. (LANL), Los Alamos, NM (United States)

    2016-06-07

    Materials Design is often at the forefront of technological innovation. While there has always been a push to generate increasingly low density materials, such as aero or hydrogels, more recently the idea of bicontinuous structures has gone more into play. This review will cover some of the methods and applications for generating both porous, and hierarchically porous structures.

  19. Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering

    Science.gov (United States)

    2010-01-01

    Background Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered), missing value imputation (2), standardization of data (2), gene selection (19) or clustering method (11). The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that background correction is

  20. Challenges in microarray class discovery: a comprehensive examination of normalization, gene selection and clustering

    Directory of Open Access Journals (Sweden)

    Landfors Mattias

    2010-10-01

    Full Text Available Abstract Background Cluster analysis, and in particular hierarchical clustering, is widely used to extract information from gene expression data. The aim is to discover new classes, or sub-classes, of either individuals or genes. Performing a cluster analysis commonly involve decisions on how to; handle missing values, standardize the data and select genes. In addition, pre-processing, involving various types of filtration and normalization procedures, can have an effect on the ability to discover biologically relevant classes. Here we consider cluster analysis in a broad sense and perform a comprehensive evaluation that covers several aspects of cluster analyses, including normalization. Result We evaluated 2780 cluster analysis methods on seven publicly available 2-channel microarray data sets with common reference designs. Each cluster analysis method differed in data normalization (5 normalizations were considered, missing value imputation (2, standardization of data (2, gene selection (19 or clustering method (11. The cluster analyses are evaluated using known classes, such as cancer types, and the adjusted Rand index. The performances of the different analyses vary between the data sets and it is difficult to give general recommendations. However, normalization, gene selection and clustering method are all variables that have a significant impact on the performance. In particular, gene selection is important and it is generally necessary to include a relatively large number of genes in order to get good performance. Selecting genes with high standard deviation or using principal component analysis are shown to be the preferred gene selection methods. Hierarchical clustering using Ward's method, k-means clustering and Mclust are the clustering methods considered in this paper that achieves the highest adjusted Rand. Normalization can have a significant positive impact on the ability to cluster individuals, and there are indications that

  1. Hierarchical Neural Regression Models for Customer Churn Prediction

    Directory of Open Access Journals (Sweden)

    Golshan Mohammadi

    2013-01-01

    Full Text Available As customers are the main assets of each industry, customer churn prediction is becoming a major task for companies to remain in competition with competitors. In the literature, the better applicability and efficiency of hierarchical data mining techniques has been reported. This paper considers three hierarchical models by combining four different data mining techniques for churn prediction, which are backpropagation artificial neural networks (ANN, self-organizing maps (SOM, alpha-cut fuzzy c-means (α-FCM, and Cox proportional hazards regression model. The hierarchical models are ANN + ANN + Cox, SOM + ANN + Cox, and α-FCM + ANN + Cox. In particular, the first component of the models aims to cluster data in two churner and nonchurner groups and also filter out unrepresentative data or outliers. Then, the clustered data as the outputs are used to assign customers to churner and nonchurner groups by the second technique. Finally, the correctly classified data are used to create Cox proportional hazards model. To evaluate the performance of the hierarchical models, an Iranian mobile dataset is considered. The experimental results show that the hierarchical models outperform the single Cox regression baseline model in terms of prediction accuracy, Types I and II errors, RMSE, and MAD metrics. In addition, the α-FCM + ANN + Cox model significantly performs better than the two other hierarchical models.

  2. The Application of Hierarchical Cluster Analysis to the Prediction of Grain Security of Small Research Areas-A Case Study of Kunshan%谱系聚类法在小区域粮食安全预测中的应用——以昆山市为例

    Institute of Scientific and Technical Information of China (English)

    姚鑫; 杨桂山; 万荣荣

    2011-01-01

    粮食安全对国民经济的可持续发展起着不可替代的基础性作用,小区域由于受政策因素的影响较大,粮食安全相关指标的变化呈一定阶段性,长时间序列的数学规律并不突出,不利于规划工作的展开.论文基于昆山市的研究,提出谱系聚类与数学模型相结合的基本思路,在此基础上推出了聚类结果有效性的量化判定标准并对聚类法运用准则做了深入的探讨.实际数据分析结果表明:昆山的粮食安全相关的社会经济指标变化确实呈明显阶段性;与利用全部时间序列数据建立的模型相比,运用谱系聚类的模型拟合和预测效果都有明显优势;至2015年,昆山市粮食自给率将下降至6%,最小人均耕地面积降低至0.022 hm2.通过进一步的分析、对比及讨论,文章认为,谱系聚类法运用于小区域粮食安全预测,方法可操作性强,结论科学性显著.%Grain security is fundamental to the sustainable development of our society and national economy. As research regions with small area are vulnerable to the impacts of policy changes, indexes related to grain security of these areas often change in the form of stages, which means that the mathematical regularity of long-term datasets is not significant. As a result, it is difficult to implement grain security programming for the future.We put forward a new method of combining hierarchical cluster analysis with traditional mathematical models, and established a quantification standard for the validity judgment of the clustering results. Meanwhile, a criterion for the using of hierarchical cluster analysis was also proposed, but we strongly recommended that mass data from other research areas are needed to calibrate and perfect it.Kunshan ( 1985 -2007 ) was chosen as a study region to prove the new method, because it is small in area but with rapid economic development. The results of analysis showed that: the indexes related to grain security did

  3. Multicollinearity in hierarchical linear models.

    Science.gov (United States)

    Yu, Han; Jiang, Shanhe; Land, Kenneth C

    2015-09-01

    This study investigates an ill-posed problem (multicollinearity) in Hierarchical Linear Models from both the data and the model perspectives. We propose an intuitive, effective approach to diagnosing the presence of multicollinearity and its remedies in this class of models. A simulation study demonstrates the impacts of multicollinearity on coefficient estimates, associated standard errors, and variance components at various levels of multicollinearity for finite sample sizes typical in social science studies. We further investigate the role multicollinearity plays at each level for estimation of coefficient parameters in terms of shrinkage. Based on these analyses, we recommend a top-down method for assessing multicollinearity in HLMs that first examines the contextual predictors (Level-2 in a two-level model) and then the individual predictors (Level-1) and uses the results for data collection, research problem redefinition, model re-specification, variable selection and estimation of a final model.

  4. Choosing appropriate analysis methods for cluster randomised cross-over trials with a binary outcome.

    Science.gov (United States)

    Morgan, Katy E; Forbes, Andrew B; Keogh, Ruth H; Jairath, Vipul; Kahan, Brennan C

    2017-01-30

    In cluster randomised cross-over (CRXO) trials, clusters receive multiple treatments in a randomised sequence over time. In such trials, there is usual correlation between patients in the same cluster. In addition, within a cluster, patients in the same period may be more similar to each other than to patients in other periods. We demonstrate that it is necessary to account for these correlations in the analysis to obtain correct Type I error rates. We then use simulation to compare different methods of analysing a binary outcome from a two-period CRXO design. Our simulations demonstrated that hierarchical models without random effects for period-within-cluster, which do not account for any extra within-period correlation, performed poorly with greatly inflated Type I errors in many scenarios. In scenarios where extra within-period correlation was present, a hierarchical model with random effects for cluster and period-within-cluster only had correct Type I errors when there were large numbers of clusters; with small numbers of clusters, the error rate was inflated. We also found that generalised estimating equations did not give correct error rates in any scenarios considered. An unweighted cluster-level summary regression performed best overall, maintaining an error rate close to 5% for all scenarios, although it lost power when extra within-period correlation was present, especially for small numbers of clusters. Results from our simulation study show that it is important to model both levels of clustering in CRXO trials, and that any extra within-period correlation should be accounted for. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

  5. An alternative interpretation of cellular 'selfish spermatogonial selection'-clusters in the human testis indicates the need for 3-D-analyses.

    Science.gov (United States)

    Pohl, E; Gromoll, J; Kliesch, S; Wistuba, J

    2016-03-01

    The 'selfish spermatogonial selection'- model was proposed to explain the paternal age effect (PAE) of some congenital disorders associated with point mutations in male germ cells. According to this, spermatogonia carrying pathogenic mutations gain a selection advantage over non-mutated spermatogonia which leads to an increased number of mutated spermatogonia and consequently spermatozoa over time. Recently, an immunohistochemical approach using the premeiotic marker melanoma antigen family A4 (MAGE A4) was undertaken by the Wilkie group to confirm the presence of microclones of putatively mutated spermatogonia in testes of elderly men. The objective of our study was the age-dependent assessment of testes from men with normal spermatogenesis using MAGE A4 immunohistochemistry to identify and corroborate cellular clusters indicative for 'selfish spermatogonial selection' in our cohort. We analyzed testicular tissues obtained from men with normal spermatogenesis assigned to three age groups [(1) 28.8 ± 2.7 years; (2) 48.1 ± 1 years; (3) 71.9 ± 6.8 years, n/group = 8]. We could detect very similar distribution patterns of MAGE A4-positive cells and the presence of several types of microclusters as reported previously. However, these cellular clusters, indicative for clonal expansion, were not only present in testes from elderly men but also in those from age group 1 and 2. Using graphical three-dimensional modelling, we identified that cross-section directions e.g. longitudinal sections might provoke misleading interpretation of spermatogonial clusters, in particular when the tissue processing is limited. Thus, appropriate fixation and embedding is needed for reliable analysis of testicular sections. We therefore propose a more careful interpretation of such spermatogonial clusters and recommend a 3-D analysis to unequivocally determine 'selfish spermatogonial selection'-manifestations.

  6. De Novo Assembly and Genome Analyses of the Marine-Derived Scopulariopsis brevicaulis Strain LF580 Unravels Life-Style Traits and Anticancerous Scopularide Biosynthetic Gene Cluster

    Science.gov (United States)

    Kumar, Abhishek; Henrissat, Bernard; Arvas, Mikko; Syed, Muhammad Fahad; Thieme, Nils; Benz, J. Philipp; Sørensen, Jens Laurids; Record, Eric; Pöggeler, Stefanie; Kempken, Frank

    2015-01-01

    The marine-derived Scopulariopsis brevicaulis strain LF580 produces scopularides A and B, which have anticancerous properties. We carried out genome sequencing using three next-generation DNA sequencing methods. De novo hybrid assembly yielded 621 scaffolds with a total size of 32.2 Mb and 16298 putative gene models. We identified a large non-ribosomal peptide synthetase gene (nrps1) and supporting pks2 gene in the same biosynthetic gene cluster. This cluster and the genes within the cluster are functionally active as confirmed by RNA-Seq. Characterization of carbohydrate-active enzymes and major facilitator superfamily (MFS)-type transporters lead to postulate S. brevicaulis originated from a soil fungus, which came into contact with the marine sponge Tethya aurantium. This marine sponge seems to provide shelter to this fungus and micro-environment suitable for its survival in the ocean. This study also builds the platform for further investigations of the role of life-style and secondary metabolites from S. brevicaulis. PMID:26505484

  7. De Novo Assembly and Genome Analyses of the Marine-Derived Scopulariopsis brevicaulis Strain LF580 Unravels Life-Style Traits and Anticancerous Scopularide Biosynthetic Gene Cluster.

    Science.gov (United States)

    Kumar, Abhishek; Henrissat, Bernard; Arvas, Mikko; Syed, Muhammad Fahad; Thieme, Nils; Benz, J Philipp; Sørensen, Jens Laurids; Record, Eric; Pöggeler, Stefanie; Kempken, Frank

    2015-01-01

    The marine-derived Scopulariopsis brevicaulis strain LF580 produces scopularides A and B, which have anticancerous properties. We carried out genome sequencing using three next-generation DNA sequencing methods. De novo hybrid assembly yielded 621 scaffolds with a total size of 32.2 Mb and 16298 putative gene models. We identified a large non-ribosomal peptide synthetase gene (nrps1) and supporting pks2 gene in the same biosynthetic gene cluster. This cluster and the genes within the cluster are functionally active as confirmed by RNA-Seq. Characterization of carbohydrate-active enzymes and major facilitator superfamily (MFS)-type transporters lead to postulate S. brevicaulis originated from a soil fungus, which came into contact with the marine sponge Tethya aurantium. This marine sponge seems to provide shelter to this fungus and micro-environment suitable for its survival in the ocean. This study also builds the platform for further investigations of the role of life-style and secondary metabolites from S. brevicaulis.

  8. Modeling the deformation behavior of nanocrystalline alloy with hierarchical microstructures

    Energy Technology Data Exchange (ETDEWEB)

    Liu, Hongxi; Zhou, Jianqiu, E-mail: zhouj@njtech.edu.cn [Nanjing Tech University, Department of Mechanical Engineering (China); Zhao, Yonghao, E-mail: yhzhao@njust.edu.cn [Nanjing University of Science and Technology, Nanostructural Materials Research Center, School of Materials Science and Engineering (China)

    2016-02-15

    A mechanism-based plasticity model based on dislocation theory is developed to describe the mechanical behavior of the hierarchical nanocrystalline alloys. The stress–strain relationship is derived by invoking the impeding effect of the intra-granular solute clusters and the inter-granular nanostructures on the dislocation movements along the sliding path. We found that the interaction between dislocations and the hierarchical microstructures contributes to the strain hardening property and greatly influence the ductility of nanocrystalline metals. The analysis indicates that the proposed model can successfully describe the enhanced strength of the nanocrystalline hierarchical alloy. Moreover, the strain hardening rate is sensitive to the volume fraction of the hierarchical microstructures. The present model provides a new perspective to design the microstructures for optimizing the mechanical properties in nanostructural metals.

  9. Classifying hospitals as mortality outliers: logistic versus hierarchical logistic models.

    Science.gov (United States)

    Alexandrescu, Roxana; Bottle, Alex; Jarman, Brian; Aylin, Paul

    2014-05-01

    The use of hierarchical logistic regression for provider profiling has been recommended due to the clustering of patients within hospitals, but has some associated difficulties. We assess changes in hospital outlier status based on standard logistic versus hierarchical logistic modelling of mortality. The study population consisted of all patients admitted to acute, non-specialist hospitals in England between 2007 and 2011 with a primary diagnosis of acute myocardial infarction, acute cerebrovascular disease or fracture of neck of femur or a primary procedure of coronary artery bypass graft or repair of abdominal aortic aneurysm. We compared standardised mortality ratios (SMRs) from non-hierarchical models with SMRs from hierarchical models, without and with shrinkage estimates of the predicted probabilities (Model 1 and Model 2). The SMRs from standard logistic and hierarchical models were highly statistically significantly correlated (r > 0.91, p = 0.01). More outliers were recorded in the standard logistic regression than hierarchical modelling only when using shrinkage estimates (Model 2): 21 hospitals (out of a cumulative number of 565 pairs of hospitals under study) changed from a low outlier and 8 hospitals changed from a high outlier based on the logistic regression to a not-an-outlier based on shrinkage estimates. Both standard logistic and hierarchical modelling have identified nearly the same hospitals as mortality outliers. The choice of methodological approach should, however, also consider whether the modelling aim is judgment or improvement, as shrinkage may be more appropriate for the former than the latter.

  10. Collaborative Hierarchical Sparse Modeling

    CERN Document Server

    Sprechmann, Pablo; Sapiro, Guillermo; Eldar, Yonina C

    2010-01-01

    Sparse modeling is a powerful framework for data analysis and processing. Traditionally, encoding in this framework is done by solving an l_1-regularized linear regression problem, usually called Lasso. In this work we first combine the sparsity-inducing property of the Lasso model, at the individual feature level, with the block-sparsity property of the group Lasso model, where sparse groups of features are jointly encoded, obtaining a sparsity pattern hierarchically structured. This results in the hierarchical Lasso, which shows important practical modeling advantages. We then extend this approach to the collaborative case, where a set of simultaneously coded signals share the same sparsity pattern at the higher (group) level but not necessarily at the lower one. Signals then share the same active groups, or classes, but not necessarily the same active set. This is very well suited for applications such as source separation. An efficient optimization procedure, which guarantees convergence to the global opt...

  11. Heuristics for Hierarchical Partitioning with Application to Model Checking

    DEFF Research Database (Denmark)

    Möller, Michael Oliver; Alur, Rajeev

    2001-01-01

    Given a collection of connected components, it is often desired to cluster together parts of strong correspondence, yielding a hierarchical structure. We address the automation of this process and apply heuristics to battle the combinatorial and computational complexity. We define a cost function...

  12. Hierarchical manifold learning.

    Science.gov (United States)

    Bhatia, Kanwal K; Rao, Anil; Price, Anthony N; Wolz, Robin; Hajnal, Jo; Rueckert, Daniel

    2012-01-01

    We present a novel method of hierarchical manifold learning which aims to automatically discover regional variations within images. This involves constructing manifolds in a hierarchy of image patches of increasing granularity, while ensuring consistency between hierarchy levels. We demonstrate its utility in two very different settings: (1) to learn the regional correlations in motion within a sequence of time-resolved images of the thoracic cavity; (2) to find discriminative regions of 3D brain images in the classification of neurodegenerative disease,

  13. ‘Feeling of despair’ as the leading cluster theme of conceptual descriptive analyses in participatory assessment: Russia Oxfam GB case study

    Directory of Open Access Journals (Sweden)

    Venera Zakirova

    2016-12-01

    Full Text Available This article provides a case study on participatory assessment based on experience gained from an EU–Oxfam GB project entitled “Empowering Municipalities to Effectively Address Poverty” conducted in five small towns in Russia. Participatory assessment through focus group discussions (FGDs was the main approach used in the implementation of the project. A participatory assessment was performed through 25 FGDs in five remote areas in central Russia. More than 200 participants representing people living in poverty, such as single mothers, people with disabilities, families with many children, families with disabled children, and pensioners, voluntarily participated in the meetings. Most of the participants were women (75% aged between 25 and 70 years. We consider that the participants’ representation is relevant in accordance with the official poverty studies. Through identification of patterns of recurrent ideas and opinions, a qualitative method helps us understand social phenomena from the views of and on the basis of the opinions of the participants. The FGDs’ narratives underwent pattern analysis, resulting in the framing of the cluster themes and narrative conceptualization. Cluster analysis of the FGDs’ narratives led to the framing of 10 cluster themes of importance, followed by conceptual descriptions and related narratives. The conceptual description of the leading theme, feeling of despair (theme 1, was described by respondents’ expressions/narratives, such as “Nobody needs us and there is no future for us and our children in this town,” the narrative idea that crosscuts the subsequent themes. The following nine themes are of equal importance, are interlinked, and for the major part constitute the leading theme, feeling of despair (theme 1: state social and family support (theme 2; health care (theme 3; who are those living in poverty? (theme 4; housing (theme 5; living costs (theme 6; employment (theme 7; children

  14. Hierarchically Structured Electrospun Fibers

    Directory of Open Access Journals (Sweden)

    Nicole E. Zander

    2013-01-01

    Full Text Available Traditional electrospun nanofibers have a myriad of applications ranging from scaffolds for tissue engineering to components of biosensors and energy harvesting devices. The generally smooth one-dimensional structure of the fibers has stood as a limitation to several interesting novel applications. Control of fiber diameter, porosity and collector geometry will be briefly discussed, as will more traditional methods for controlling fiber morphology and fiber mat architecture. The remainder of the review will focus on new techniques to prepare hierarchically structured fibers. Fibers with hierarchical primary structures—including helical, buckled, and beads-on-a-string fibers, as well as fibers with secondary structures, such as nanopores, nanopillars, nanorods, and internally structured fibers and their applications—will be discussed. These new materials with helical/buckled morphology are expected to possess unique optical and mechanical properties with possible applications for negative refractive index materials, highly stretchable/high-tensile-strength materials, and components in microelectromechanical devices. Core-shell type fibers enable a much wider variety of materials to be electrospun and are expected to be widely applied in the sensing, drug delivery/controlled release fields, and in the encapsulation of live cells for biological applications. Materials with a hierarchical secondary structure are expected to provide new superhydrophobic and self-cleaning materials.

  15. HDS: Hierarchical Data System

    Science.gov (United States)

    Pearce, Dave; Walter, Anton; Lupton, W. F.; Warren-Smith, Rodney F.; Lawden, Mike; McIlwrath, Brian; Peden, J. C. M.; Jenness, Tim; Draper, Peter W.

    2015-02-01

    The Hierarchical Data System (HDS) is a file-based hierarchical data system designed for the storage of a wide variety of information. It is particularly suited to the storage of large multi-dimensional arrays (with their ancillary data) where efficient access is needed. It is a key component of the Starlink software collection (ascl:1110.012) and is used by the Starlink N-Dimensional Data Format (NDF) library (ascl:1411.023). HDS organizes data into hierarchies, broadly similar to the directory structure of a hierarchical filing system, but contained within a single HDS container file. The structures stored in these files are self-describing and flexible; HDS supports modification and extension of structures previously created, as well as functions such as deletion, copying, and renaming. All information stored in HDS files is portable between the machines on which HDS is implemented. Thus, there are no format conversion problems when moving between machines. HDS can write files in a private binary format (version 4), or be layered on top of HDF5 (version 5).

  16. Hierarchical Scaling in Systems of Natural Cities

    CERN Document Server

    Chen, Yanguang

    2016-01-01

    Hierarchies can be modeled by a set of exponential functions, from which we can derive a set of power laws indicative of scaling. These scaling laws are followed by many natural and social phenomena such as cities, earthquakes, and rivers. This paper is devoted to revealing the scaling patterns in systems of natural cities by reconstructing the hierarchy with cascade structure. The cities of America, Britain, France, and Germany are taken as examples to make empirical analyses. The hierarchical scaling relations can be well fitted to the data points within the scaling ranges of the size and area of the natural cities. The size-number and area-number scaling exponents are close to 1, and the allometric scaling exponent is slightly less than 1. The results suggest that natural cities follow hierarchical scaling laws and hierarchical conservation law. Zipf's law proved to be one of the indications of the hierarchical scaling, and the primate law of city-size distribution represents a local pattern and can be mer...

  17. Visual Analytics for Spatial Clusters of Air-Quality Data.

    Science.gov (United States)

    Zhou, Zhiguang; Ye, Zhifei; Liu, Yanan; Liu, Fang; Tao, Yubo; Su, Weihua

    2017-01-01

    With the rapid development of industrial society, air pollution has become a major issue in the modern world. The development and widespread deployment of sensors has enabled the collection of air-quality datasets with detailed spatial and temporal scales. Analyses of these spatiotemporal air-quality datasets can help decision makers explore the major causes of air pollution and find efficient solutions. The authors designed a visual analytics system that uses multidimensional scaling (MDS) to transform the air-quality data from monitor stations into 2D plots and uses hierarchical clustering, Voronoi diagrams, and storyline visualizations to help experts explore various attributes and time scales in the data.

  18. Detecting Hierarchical Structure in Networks

    DEFF Research Database (Denmark)

    Herlau, Tue; Mørup, Morten; Schmidt, Mikkel Nørgaard;

    2012-01-01

    a generative Bayesian model that is able to infer whether hierarchies are present or not from a hypothesis space encompassing all types of hierarchical tree structures. For efficient inference we propose a collapsed Gibbs sampling procedure that jointly infers a partition and its hierarchical structure......Many real-world networks exhibit hierarchical organization. Previous models of hierarchies within relational data has focused on binary trees; however, for many networks it is unknown whether there is hierarchical structure, and if there is, a binary tree might not account well for it. We propose....... On synthetic and real data we demonstrate that our model can detect hierarchical structure leading to better link-prediction than competing models. Our model can be used to detect if a network exhibits hierarchical structure, thereby leading to a better comprehension and statistical account the network....

  19. Context updates are hierarchical

    Directory of Open Access Journals (Sweden)

    Anton Karl Ingason

    2016-10-01

    Full Text Available This squib studies the order in which elements are added to the shared context of interlocutors in a conversation. It focuses on context updates within one hierarchical structure and argues that structurally higher elements are entered into the context before lower elements, even if the structurally higher elements are pronounced after the lower elements. The crucial data are drawn from a comparison of relative clauses in two head-initial languages, English and Icelandic, and two head-final languages, Korean and Japanese. The findings have consequences for any theory of a dynamic semantics.

  20. The structure of dust aggregates in hierarchical coagulation

    CERN Document Server

    Dominik, Carsten; Borel, Herman

    2016-01-01

    Dust coagulation in interstellar space and protoplanetary disks is usually treated as one of 2 extreme cases: Particle-Cluster Aggregation and Cluster-Cluster Aggregation. In this paper we study the process of hierarchical growth, where aggregates are built from significantly smaller aggregates (but not monomers). We show that this process can be understood as a modified, PCA-like process that produces porous, but non-fractal particles whose filling factor is chiefly determined by the porosity of the building blocks. We also show that in a coagulation environment where relative velocities are driven by turbulence, a logarithmically flat mass distribution (equal mass per mass decade) as it is typically found in environments where fragmentation replenishes small grains, leads to a situation where small particles and aggregates dominate the growth of large ones. Therefore, in such environments, hierarchical growth should be seen as the norm. Consequently, we predict that the aggregates in such environments are n...

  1. Dhurrin metabolism in the developing grain of Sorghum bicolor (L.) Moench investigated by metabolite profiling and novel clustering analyses of time-resolved transcriptomic data

    DEFF Research Database (Denmark)

    Nielsen, Lasse Janniche; Stuart, Peter; Pičmanová, Martina;

    2016-01-01

    Background: The important cereal crop Sorghum bicolor (L.) Moench biosynthesize and accumulate the defensive compound dhurrin during development. Previous work has suggested multiple roles for the compound including a function as nitrogen storage/buffer. Crucial for this function is the endogenous...... turnover of dhurrin for which putative pathways have been suggested but not confirmed. Results: In this study, the biosynthesis and endogenous turnover of dhurrin in the developing sorghum grain was studied by metabolite profiling and time-resolved transcriptome analyses. Dhurrin was found to accumulate...... analyses coupled with metabolite profiling, identified gene candidates involved in proanthocyanidin biosynthesis in sorghum. Conclusions: The results presented in this article reveal the existence of two endogenous dhurrin turnover pathways in sorghum, identify genes putatively involved...

  2. Hierarchical self-assembly of nanoparticles in polymer matrix and the nature of the interparticle interaction

    Science.gov (United States)

    Lin, Yu-Chiao; Chen, Chun-Yu; Chen, Hsin-Lung; Hashimoto, Takeji; Chen, Show-An; Li, Yen-Cheng

    2015-06-01

    Using small angle X-ray scattering (SAXS), we elucidated the spatial organization of palladium (Pd) nanoparticles (NPs) in the polymer matrix of poly(2-vinylpyridine) (P2VP) and the nature of inter-nanoparticle interactions, where the NPs were synthesized in the presence of P2VP by the reduction of palladium acetylacetonate (Pd(acac)2). The experimental SAXS profiles were analysed on the basis of a hierarchical structure model considering the following two types of interparticle potential: (i) hard-core repulsion only (i.e., the hard-sphere interaction) and (ii) hard-core repulsion together with an attractive potential well (i.e., the sticky hard-sphere interaction). The corresponding theoretical scattering functions, which were used for analysing the experimental SAXS profiles, were obtained within the context of the Percus-Yevick closure and the Ornstein-Zernike equation in the fundamental liquid theory. The analyses revealed that existence of the attractive potential well is indispensable to account for the experimental SAXS profiles. Moreover, the morphology of the hybrids was found to be characterized by a hierarchical structure with three levels, where about six primary NPs with the diameter of ca. 1.8 nm (level one) formed local clusters (level two), and these clusters aggregated to build up a large-scale mass-fractal structure (level three) with the fractal dimension of ca. 2.3. The scattering function developed here is of general use for quantitatively characterizing the morphological structures of polymer/NP hybrids and, in particular, for exploring the interaction potential of the NPs on the basis of the fundamental liquid theory.

  3. A hierarchical approach to forest landscape pattern characterization.

    Science.gov (United States)

    Wang, Jialing; Yang, Xiaojun

    2012-01-01

    Landscape spatial patterns have increasingly been considered to be essential for environmental planning and resources management. In this study, we proposed a hierarchical approach for landscape classification and evaluation by characterizing landscape spatial patterns across different hierarchical levels. The case study site is the Red Hills region of northern Florida and southwestern Georgia, well known for its biodiversity, historic resources, and scenic beauty. We used one Landsat Enhanced Thematic Mapper image to extract land-use/-cover information. Then, we employed principal-component analysis to help identify key class-level landscape metrics for forests at different hierarchical levels, namely, open pine, upland pine, and forest as a whole. We found that the key class-level landscape metrics varied across different hierarchical levels. Compared with forest as a whole, open pine forest is much more fragmented. The landscape metric, such as CONTIG_MN, which measures whether pine patches are contiguous or not, is more important to characterize the spatial pattern of pine forest than to forest as a whole. This suggests that different metric sets should be used to characterize landscape patterns at different hierarchical levels. We further used these key metrics, along with the total class area, to classify and evaluate subwatersheds through cluster analysis. This study demonstrates a promising approach that can be used to integrate spatial patterns and processes for hierarchical forest landscape planning and management.

  4. Hierarchical Dragonfly Wing: Microstructure-Biomechanical Behavior Relations

    Institute of Scientific and Technical Information of China (English)

    Yinglong Chen; Xishu Wang; Huaihui Ren; Hang Yin; Su Jia

    2012-01-01

    The dragonfly wing,which consists of veins and membrane,is of biological hierarchical material.We observed the cross-sections of longitudinal veins and membrane using Environmental Scanning Electron Microscopy (ESEM).Based on the experiments and previous studies,we described the longitudinal vein and the membrane in terms of two hierarchical levels of organization of composite materials at the micro- and nano-scales.The longitudinal vein of dragonfly wing has a complex sandwich structure with two chitinous shells and a protein layer,and it is considered as the first hierarchical level of the vein.Moreover,the chitinous shells are concentric multilayered structures.Clusters of nano-fibrils grow along the circumferential orientation embedded into the protein layer.It is considered as the second level of the hierarchy.Similarly,the upper and lower epidermises of membrane constitute the first hierarchical level of organization in micro scale.Similar to the vein shell,the membrane epidermises were found to be a paralleled multilayered structure,defined as the second hierarchical level of the membrane.Combining with the mechanical behavior analysis of the dragonfly wing,we concluded that the growth orientation of the hierarchical structure of the longitudinal vein and membrane is relevant to its biomechanical behavior.

  5. Hierarchical Star Formation in Nearby LEGUS Galaxies

    CERN Document Server

    Elmegreen, Debra Meloy; Adamo, Angela; Aloisi, Alessandra; Andrews, Jennifer; Annibali, Francesca; Bright, Stacey N; Calzetti, Daniela; Cignoni, Michele; Evans, Aaron S; Gallagher, John S; Gouliermis, Dimitrios A; Grebel, Eva K; Hunter, Deidre A; Johnson, Kelsey; Kim, Hwi; Lee, Janice; Sabbi, Elena; Smith, Linda; Thilker, David; Tosi, Monica; Ubeda, Leonardo

    2014-01-01

    Hierarchical structure in ultraviolet images of 12 late-type LEGUS galaxies is studied by determining the numbers and fluxes of nested regions as a function of size from ~1 to ~200 pc, and the number as a function of flux. Two starburst dwarfs, NGC 1705 and NGC 5253, have steeper number-size and flux-size distributions than the others, indicating high fractions of the projected areas filled with star formation. Nine subregions in 7 galaxies have similarly steep number-size slopes, even when the whole galaxies have shallower slopes. The results suggest that hierarchically structured star-forming regions several hundred parsecs or larger represent common unit structures. Small galaxies dominated by only a few of these units tend to be starbursts. The self-similarity of young stellar structures down to parsec scales suggests that star clusters form in the densest parts of a turbulent medium that also forms loose stellar groupings on larger scales. The presence of super star clusters in two of our starburst dwarf...

  6. Two Different Protocols for Knee Joint Motion Analyses in the Stance Phase of Gait: Correlation of the Rigid Marker Set and the Point Cluster Technique

    Directory of Open Access Journals (Sweden)

    Takashi Fukaya

    2012-01-01

    Full Text Available Objective. There are no reports comparing the protocols provided by rigid marker set (RMS and point cluster technique (PCT, which are similar in terms of estimating anatomical landmarks based on markers attached to a segment. The purpose of this study was to clarify the correlation of the two different protocols, which are protocols for knee motion in gait, and identify whether measurement errors arose at particular periods during the stance phase. Methods. The study subjects were 10 healthy adults. All estimated anatomical landmarks were which their positions, calculated by each protocol of the PCT and RMS, were compared using Pearson’s product correlation coefficients. To examine the reliability of the angle changes of the knee joint measured by RMS and the PCT, the coefficient of multiple correlations (CMCs was used. Results. Although the estimates of the anatomical landmarks showed high correlations of >0.90 (<0.01 for the Y- and Z-coordinates, the correlations were low for the X-coordinates at all anatomical landmarks. The CMC was 0.94 for flexion/extension, 0.74 for abduction/adduction, and 0.71 for external/internal rotation. Conclusion. Flexion/extension and abduction/adduction of the knee by two different protocols had comparatively little error and good reliability after 30% of the stance phase.

  7. Dependence of X-Ray Luminosity on Temperature for Groups and Clusters with the Moving Median Statistics

    Institute of Scientific and Technical Information of China (English)

    陈黎

    2002-01-01

    We re-analyse the relationship between the x-ray luminosity (Lx ) and the temperature (T) of groups and clusters of galaxies, based on the largest sample of 40 groups and 188 clusters. We employ the moving median statistics for the data set, along with the proper linear regression. Our newly established Lx - T relations for groups and clusters show no significant difference within statistical uncertainties, yielding LX ∝ T2.79±0.01 (groups) and LX ∝ T2.54±0.004 (clusters). This also supports the hierarchical scenario of structure formation in which groups are simply the scale-down version of clusters. It is argued that the break in the Lx - T relation on group scales detected in previous studies may suffer from sparse data sample and poor statistical methods.

  8. IDENTIFICAÇÃO DE CLUSTERS INTERNACIONAIS COM BASE NAS DIMENSÕES CULTURAIS DE HOFSTEDE. / Identification of international clusters based on the hofstede’s cultural dimensions

    Directory of Open Access Journals (Sweden)

    Valderí de Castro Alcântara1

    2012-08-01

    Full Text Available Haja vista que a cultura de um país influencia a cultura organizacional das empresas nele presente e ainda é fator determinante no processo de internacionalização, torna-se relevante compreender e mensurar as características culturais de cada país. Os estudos de Hofstede (1984 apresentam uma metodologia útil para comparação entre culturas. Tal metodologia leva em consideração as características deuma cultura que possibilita diferenciar um país de outro. Dessa forma, é possível observar que determinados países compartilham certos traços culturais e, assim, é possível agrupá-los segundo critérios pré-estabelecidos. O presente trabalho objetiva utilizar-se de procedimentos estatísticos multivariados Clusters Analyses, K-Means Cluster Analysis e Análise Discriminante para determinar e validar agrupamentos de países, com base nas dimensões culturais de Hofstede (Distance Index, Individualism, Masculinity e Uncertainty Avoidance Index. Os resultados determinaram quatro clusters: Cluster 1 - países com cultura masculina e individualista; Cluster 2 - cultura coletivista e aversa à incerteza; Cluster 3 - cultura feminina e com baixa distância hierárquica; e Cluster 4 - cultura com elevada distância hierárquica e propensão à incerteza./ Considering that the culture of a country influences the organizational culture of this company and it is still a determining factor in the internationalization process becomes important to understand and measure the cultural characteristics of each country. The studies of Hofstede (1984 present a useful methodology for comparing cultures, this methodology takes into account the characteristics of a culturethat allows to differentiate one from another country. Thus one can observe that certain countries share certain cultural traits and so it is possible grouping them according to predetermined criteria. The present work aims to utilize multivariate statistical procedures Cluster Analyses

  9. Detect overlapping and hierarchical community structure in networks

    CERN Document Server

    Shen, Huawei; Cai, Kai; Hu, Mao-Bin

    2008-01-01

    Clustering and community structure is crucial for many network systems and the related dynamic processes. It has been shown that communities are usually overlapping and hierarchical. However, previous methods investigate these two properties of community structure separately. This paper propose an algorithm (EAGLE) to detect both the overlapping and hierarchical properties of complex community structure together. This algorithm deals with the set of maximal cliques and adopts an agglomerative framework. The quality function of modularity is extended to evaluate the goodness of a cover. The examples of application to real world networks give excellent results.

  10. Design of Hierarchical Structures for Synchronized Deformations

    Science.gov (United States)

    Seifi, Hamed; Javan, Anooshe Rezaee; Ghaedizadeh, Arash; Shen, Jianhu; Xu, Shanqing; Xie, Yi Min

    2017-01-01

    In this paper we propose a general method for creating a new type of hierarchical structures at any level in both 2D and 3D. A simple rule based on a rotate-and-mirror procedure is introduced to achieve multi-level hierarchies. These new hierarchical structures have remarkably few degrees of freedom compared to existing designs by other methods. More importantly, these structures exhibit synchronized motions during opening or closure, resulting in uniform and easily-controllable deformations. Furthermore, a simple analytical formula is found which can be used to avoid collision of units of the structure during the closing process. The novel design concept is verified by mathematical analyses, computational simulations and physical experiments.

  11. clusterMaker: a multi-algorithm clustering plugin for Cytoscape

    Science.gov (United States)

    2011-01-01

    Background In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view), k-means, k-medoid, SCPS, AutoSOME, and native (Java) MCL. Results Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC) enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. Conclusions The Cytoscape plugin clusterMaker provides a number of clustering

  12. clusterMaker: a multi-algorithm clustering plugin for Cytoscape

    Directory of Open Access Journals (Sweden)

    Morris John H

    2011-11-01

    Full Text Available Abstract Background In the post-genomic era, the rapid increase in high-throughput data calls for computational tools capable of integrating data of diverse types and facilitating recognition of biologically meaningful patterns within them. For example, protein-protein interaction data sets have been clustered to identify stable complexes, but scientists lack easily accessible tools to facilitate combined analyses of multiple data sets from different types of experiments. Here we present clusterMaker, a Cytoscape plugin that implements several clustering algorithms and provides network, dendrogram, and heat map views of the results. The Cytoscape network is linked to all of the other views, so that a selection in one is immediately reflected in the others. clusterMaker is the first Cytoscape plugin to implement such a wide variety of clustering algorithms and visualizations, including the only implementations of hierarchical clustering, dendrogram plus heat map visualization (tree view, k-means, k-medoid, SCPS, AutoSOME, and native (Java MCL. Results Results are presented in the form of three scenarios of use: analysis of protein expression data using a recently published mouse interactome and a mouse microarray data set of nearly one hundred diverse cell/tissue types; the identification of protein complexes in the yeast Saccharomyces cerevisiae; and the cluster analysis of the vicinal oxygen chelate (VOC enzyme superfamily. For scenario one, we explore functionally enriched mouse interactomes specific to particular cellular phenotypes and apply fuzzy clustering. For scenario two, we explore the prefoldin complex in detail using both physical and genetic interaction clusters. For scenario three, we explore the possible annotation of a protein as a methylmalonyl-CoA epimerase within the VOC superfamily. Cytoscape session files for all three scenarios are provided in the Additional Files section. Conclusions The Cytoscape plugin cluster

  13. A quantitative method to analyse an open-ended questionnaire: A case study about the Boltzmann Factor

    Science.gov (United States)

    Rosario Battaglia, Onofrio; Di Paola, Benedetto

    2016-05-01

    This paper describes a quantitative method to analyse an open-ended questionnaire. Student responses to a specially designed written questionnaire are quantitatively analysed by not hierarchical clustering called k -means method. Through this we can characterise behaviour students with respect their expertise to formulate explanations for phenomena or processes and/or use a given model in the different context. The physics topic is about the Boltzmann Factor, which allows the students to have a unifying view of different phenomena in different contexts.

  14. Semi-supervised consensus clustering for gene expression data analysis

    OpenAIRE

    Wang, Yunli; Pan, Youlian

    2014-01-01

    Background Simple clustering methods such as hierarchical clustering and k-means are widely used for gene expression data analysis; but they are unable to deal with noise and high dimensionality associated with the microarray gene expression data. Consensus clustering appears to improve the robustness and quality of clustering results. Incorporating prior knowledge in clustering process (semi-supervised clustering) has been shown to improve the consistency between the data partitioning and do...

  15. What Makes Clusters Decline?

    DEFF Research Database (Denmark)

    Østergaard, Christian Richter; Park, Eun Kyung

    2015-01-01

    Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark....... The longitudinal study on the high-tech cluster reveals that technological lock-in and exit of key firms have contributed to decline. Entrepreneurship has a positive effect on the cluster’s adaptive capabilities, while multinational companies have contradicting effects by bringing in new resources to the cluster...

  16. Comparison of genetic diversity structure analyses of SSR molecular marker data within apple (Malus×domestica) genetic resources.

    Science.gov (United States)

    Patzak, Josef; Paprštein, František; Henychová, Alena; Sedlák, Jiří

    2012-09-01

    The aim of this study was to compare traditional hierarchical clustering techniques and principal coordinate analysis (PCoA) with the model-based Bayesian cluster analyses in relation to subpopulation differentiation based on breeding history and geographical origin of apple (Malus×domestica Borkh.) cultivars and landraces. We presented the use of a set of 10 microsatellite (SSR) loci for genetic diversity structure analyses of 273 apple accessions from national genetic resources. These SSR loci yielded a total of 113 polymorphic SSR alleles, with 5-18 alleles per locus. SSR molecular data were successfully used in binary and allelic input format for all genetic diversity analyses, but allelic molecular data did not reveal reliable results with the NTSYS-pc and BAPS softwares. A traditional cluster analysis still provided an easy and effective way for determining genetic diversity structure in the apple germplasm collection. A model-based Bayesian analysis also provided the clustering results in accordance to traditional cluster analysis, but the analyses were distorted by the presence of a dominant group of apple genetic resources owing to the narrow origin of the apple genome. PCoA confirmed that there were no noticeable differences in genetic diversity structure of apple genetic resources during the breeding history. The results of our analyses are useful in the context of enhancing apple collection management, sampling of core collections, and improving breeding processes.

  17. Exploitation of Clustering Techniques in Transactional Healthcare Data

    Directory of Open Access Journals (Sweden)

    Naeem Ahmad Mahoto

    2014-03-01

    Full Text Available Healthcare service centres equipped with electronic health systems have improved their resources as well as treatment processes. The dynamic nature of healthcare data of each individual makes it complex and difficult for physicians to manually mediate them; therefore, automatic techniques are essential to manage the quality and standardization of treatment procedures. Exploratory data analysis, patternanalysis and grouping of data is managed using clustering techniques, which work as an unsupervised classification. A number of healthcare applications are developed that use several data mining techniques for classification, clustering and extracting useful information from healthcare data. The challenging issue in this domain is to select adequate data mining algorithm for optimal results. This paper exploits three different clustering algorithms: DBSCAN (Density-Based Clustering, agglomerative hierarchical and k-means in real transactional healthcare data of diabetic patients (taken as case study to analyse their performance in large and dispersed healthcare data. The best solution of cluster sets among the exploited algorithms is evaluated using clustering quality indexes and is selected to identify the possible subgroups of patients having similar treatment patterns

  18. Discrepancy-Tolerant Hierarchical Poisson Event-Rate Analyses.

    Science.gov (United States)

    1985-07-01

    the Nuclear Plant Reliability Data System." Austin, Texas: The Univ. of Texas. NUREG /CR-3637. 41 Hoaglin, G.C. (1983). "g and h distributions... NUREG /CR-2434, LA-9116-MS. Morris, C. (1982). "Natural exponential families with quadratic variance functions: statistical theory." Annals of Statistics...al (1975). "Reactor Safety Study: An Assessment of Accident Risks in U.S. Commercial Nuclear Power Plants." NUREG -75/014, WASH 1400. Reynolds, D.S

  19. Hierarchical partial order ranking.

    Science.gov (United States)

    Carlsen, Lars

    2008-09-01

    Assessing the potential impact on environmental and human health from the production and use of chemicals or from polluted sites involves a multi-criteria evaluation scheme. A priori several parameters are to address, e.g., production tonnage, specific release scenarios, geographical and site-specific factors in addition to various substance dependent parameters. Further socio-economic factors may be taken into consideration. The number of parameters to be included may well appear to be prohibitive for developing a sensible model. The study introduces hierarchical partial order ranking (HPOR) that remedies this problem. By HPOR the original parameters are initially grouped based on their mutual connection and a set of meta-descriptors is derived representing the ranking corresponding to the single groups of descriptors, respectively. A second partial order ranking is carried out based on the meta-descriptors, the final ranking being disclosed though average ranks. An illustrative example on the prioritization of polluted sites is given.

  20. Trees and Hierarchical Structures

    CERN Document Server

    Haeseler, Arndt

    1990-01-01

    The "raison d'etre" of hierarchical dustering theory stems from one basic phe­ nomenon: This is the notorious non-transitivity of similarity relations. In spite of the fact that very often two objects may be quite similar to a third without being that similar to each other, one still wants to dassify objects according to their similarity. This should be achieved by grouping them into a hierarchy of non-overlapping dusters such that any two objects in ~ne duster appear to be more related to each other than they are to objects outside this duster. In everyday life, as well as in essentially every field of scientific investigation, there is an urge to reduce complexity by recognizing and establishing reasonable das­ sification schemes. Unfortunately, this is counterbalanced by the experience of seemingly unavoidable deadlocks caused by the existence of sequences of objects, each comparatively similar to the next, but the last rather different from the first.

  1. Optimisation by hierarchical search

    Science.gov (United States)

    Zintchenko, Ilia; Hastings, Matthew; Troyer, Matthias

    2015-03-01

    Finding optimal values for a set of variables relative to a cost function gives rise to some of the hardest problems in physics, computer science and applied mathematics. Although often very simple in their formulation, these problems have a complex cost function landscape which prevents currently known algorithms from efficiently finding the global optimum. Countless techniques have been proposed to partially circumvent this problem, but an efficient method is yet to be found. We present a heuristic, general purpose approach to potentially improve the performance of conventional algorithms or special purpose hardware devices by optimising groups of variables in a hierarchical way. We apply this approach to problems in combinatorial optimisation, machine learning and other fields.

  2. How hierarchical is language use?

    Science.gov (United States)

    Frank, Stefan L.; Bod, Rens; Christiansen, Morten H.

    2012-01-01

    It is generally assumed that hierarchical phrase structure plays a central role in human language. However, considerations of simplicity and evolutionary continuity suggest that hierarchical structure should not be invoked too hastily. Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. In this paper, we review evidence from the recent literature supporting the hypothesis that sequential structure may be fundamental to the comprehension, production and acquisition of human language. Moreover, we provide a preliminary sketch outlining a non-hierarchical model of language use and discuss its implications and testable predictions. If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science. PMID:22977157

  3. How hierarchical is language use?

    Science.gov (United States)

    Frank, Stefan L; Bod, Rens; Christiansen, Morten H

    2012-11-22

    It is generally assumed that hierarchical phrase structure plays a central role in human language. However, considerations of simplicity and evolutionary continuity suggest that hierarchical structure should not be invoked too hastily. Indeed, recent neurophysiological, behavioural and computational studies show that sequential sentence structure has considerable explanatory power and that hierarchical processing is often not involved. In this paper, we review evidence from the recent literature supporting the hypothesis that sequential structure may be fundamental to the comprehension, production and acquisition of human language. Moreover, we provide a preliminary sketch outlining a non-hierarchical model of language use and discuss its implications and testable predictions. If linguistic phenomena can be explained by sequential rather than hierarchical structure, this will have considerable impact in a wide range of fields, such as linguistics, ethology, cognitive neuroscience, psychology and computer science.

  4. Associative Hierarchical Random Fields.

    Science.gov (United States)

    Ladický, L'ubor; Russell, Chris; Kohli, Pushmeet; Torr, Philip H S

    2014-06-01

    This paper makes two contributions: the first is the proposal of a new model-The associative hierarchical random field (AHRF), and a novel algorithm for its optimization; the second is the application of this model to the problem of semantic segmentation. Most methods for semantic segmentation are formulated as a labeling problem for variables that might correspond to either pixels or segments such as super-pixels. It is well known that the generation of super pixel segmentations is not unique. This has motivated many researchers to use multiple super pixel segmentations for problems such as semantic segmentation or single view reconstruction. These super-pixels have not yet been combined in a principled manner, this is a difficult problem, as they may overlap, or be nested in such a way that the segmentations form a segmentation tree. Our new hierarchical random field model allows information from all of the multiple segmentations to contribute to a global energy. MAP inference in this model can be performed efficiently using powerful graph cut based move making algorithms. Our framework generalizes much of the previous work based on pixels or segments, and the resulting labelings can be viewed both as a detailed segmentation at the pixel level, or at the other extreme, as a segment selector that pieces together a solution like a jigsaw, selecting the best segments from different segmentations as pieces. We evaluate its performance on some of the most challenging data sets for object class segmentation, and show that this ability to perform inference using multiple overlapping segmentations leads to state-of-the-art results.

  5. Adaptive color visualization for dichromats using a customized hierarchical palette

    Science.gov (United States)

    Rodríguez-Pardo, Carlos E.; Sharma, Gaurav

    2011-01-01

    We propose a user-centric methodology for displaying digital color documents, that optimizes color representations in an observer specific and adaptive fashion. We apply our framework to situations involving viewers with common dichromatic color vision deficiencies, who face challenges in perceiving information presented in color images and graphics designed for color normal individuals. For situations involving qualitative data visualization, we present a computationally efficient solution that combines a customized observer-specific hierarchical palette with "display time" selection of the number of colors to generate renderings with colors that are easily discriminated by the intended viewer. The palette design is accomplished via a clustering algorithm, that arranges colors in a hierarchical tree based on their perceived differences for the intended viewer. A desired number of highly discriminable colors are readily obtained from the hierarchical palette via a simple truncation. As an illustration, we demonstrate the application of the methodology to Ishihara style images.

  6. Modelling hierarchical and modular complex networks: division and independence

    Science.gov (United States)

    Kim, D.-H.; Rodgers, G. J.; Kahng, B.; Kim, D.

    2005-06-01

    We introduce a growing network model which generates both modular and hierarchical structure in a self-organized way. To this end, we modify the Barabási-Albert model into the one evolving under the principles of division and independence as well as growth and preferential attachment (PA). A newly added vertex chooses one of the modules composed of existing vertices, and attaches edges to vertices belonging to that module following the PA rule. When the module size reaches a proper size, the module is divided into two, and a new module is created. The karate club network studied by Zachary is a simple version of the current model. We find that the model can reproduce both modular and hierarchical properties, characterized by the hierarchical clustering function of a vertex with degree k, C(k), being in good agreement with empirical measurements for real-world networks.

  7. Modeling hierarchical structures - Hierarchical Linear Modeling using MPlus

    CERN Document Server

    Jelonek, M

    2006-01-01

    The aim of this paper is to present the technique (and its linkage with physics) of overcoming problems connected to modeling social structures, which are typically hierarchical. Hierarchical Linear Models provide a conceptual and statistical mechanism for drawing conclusions regarding the influence of phenomena at different levels of analysis. In the social sciences it is used to analyze many problems such as educational, organizational or market dilemma. This paper introduces the logic of modeling hierarchical linear equations and estimation based on MPlus software. I present my own model to illustrate the impact of different factors on school acceptation level.

  8. The Large-Scale Environment of Dynamical Young Clusters of Galaxies

    OpenAIRE

    Plionis, M.; Basilakos, S.

    2001-01-01

    We investigate whether the dynamical status of clusters is related to the large-scale structure of the Universe. We find that cluster substructure is strongly correlated with the tendency of clusters to be aligned with their nearest neighbour and in general with the nearby clusters that belong to the same supercluster. Furthermore, dynamically young clusters are more clustered than the overall cluster population. These are strong indications that clusters develop in a hierarchical fashion by ...

  9. Data Preprocessing in Cluster Analysis of Gene Expression

    Institute of Scientific and Technical Information of China (English)

    杨春梅; 万柏坤; 高晓峰

    2003-01-01

    Considering that the DNA microarray technology has generated explosive gene expression data and that it is urgent to analyse and to visualize such massive datasets with efficient methods, we investigate the data preprocessing methods used in cluster analysis, normalization or logarithm of the matrix, by using hierarchical clustering, principal component analysis (PCA) and self-organizing maps (SOMs). The results illustrate that when using the Euclidean distance as measuring metrics, logarithm of relative expression level is the best preprocessing method, while data preprocessed by normalization cannot attain the expected results because the data structure is ruined. If there are only a few principal components, the PCA is an effective method to extract the frame structure, while SOMs are more suitable for a specific structure.

  10. Mechanics of hierarchical 3-D nanofoams

    Science.gov (United States)

    Chen, Q.; Pugno, N. M.

    2012-01-01

    In this paper, we study the mechanics of new three-dimensional hierarchical open-cell foams, and, in particular, its Young's modulus and plastic strength. We incorporate the effects of the surface elasticity and surface residual stress in the linear elastic and plastic analyses. The results show that, as the cross-sectional dimension decreases, the influences of the surface effect on Young's modulus and plastic strength increase, and the surface effect makes the solid stiffer and stronger; similarly, as level n increases, these quantities approach to those of the classical theory as lower bounds.

  11. Modeling hierarchical structures - Hierarchical Linear Modeling using MPlus

    OpenAIRE

    Jelonek, Magdalena

    2006-01-01

    The aim of this paper is to present the technique (and its linkage with physics) of overcoming problems connected to modeling social structures, which are typically hierarchical. Hierarchical Linear Models provide a conceptual and statistical mechanism for drawing conclusions regarding the influence of phenomena at different levels of analysis. In the social sciences it is used to analyze many problems such as educational, organizational or market dilemma. This paper introduces the logic of m...

  12. Hierarchical surfaces for enhanced self-cleaning applications

    Science.gov (United States)

    Fernández, Ariadna; Francone, Achille; Thamdrup, Lasse H.; Johansson, Alicia; Bilenberg, Brian; Nielsen, Theodor; Guttmann, Markus; Sotomayor Torres, Clivia M.; Kehagias, Nikolaos

    2017-04-01

    In this study we present a flexible and adaptable fabrication method to create complex hierarchical structures over inherently hydrophobic resist materials. We have tested these surfaces for their superhydrophobic behaviour and successfully verified their self-cleaning properties. The followed approach allow us to design and produce superhydrophobic surfaces in a reproducible manner. We have analysed different combination of hierarchical micro-nanostructures for their application to self-cleaning surfaces. A static contact angle value of 170° with a hysteresis of 4° was achieved without the need of any additional chemical treatment on the fabricated hierarchical structures. Dynamic effects were analysed on these surfaces, obtaining a remarkable self-cleaning effect as well as a good robustness over impacting droplets.

  13. Hierarchical fringe tracking

    CERN Document Server

    Petrov, Romain G; Boskri, Abdelkarim; Folcher, Jean-Pierre; Lagarde, Stephane; Bresson, Yves; Benkhaldoum, Zouhair; Lazrek, Mohamed; Rakshit, Suvendu

    2014-01-01

    The limiting magnitude is a key issue for optical interferometry. Pairwise fringe trackers based on the integrated optics concepts used for example in GRAVITY seem limited to about K=10.5 with the 8m Unit Telescopes of the VLTI, and there is a general "common sense" statement that the efficiency of fringe tracking, and hence the sensitivity of optical interferometry, must decrease as the number of apertures increases, at least in the near infrared where we are still limited by detector readout noise. Here we present a Hierarchical Fringe Tracking (HFT) concept with sensitivity at least equal to this of a two apertures fringe trackers. HFT is based of the combination of the apertures in pairs, then in pairs of pairs then in pairs of groups. The key HFT module is a device that behaves like a spatial filter for two telescopes (2TSF) and transmits all or most of the flux of a cophased pair in a single mode beam. We give an example of such an achromatic 2TSF, based on very broadband dispersed fringes analyzed by g...

  14. Onboard hierarchical network

    Science.gov (United States)

    Tunesi, Luca; Armbruster, Philippe

    2004-02-01

    The objective of this paper is to demonstrate a suitable hierarchical networking solution to improve capabilities and performances of space systems, with significant recurrent costs saving and more efficient design & manufacturing flows. Classically, a satellite can be split in two functional sub-systems: the platform and the payload complement. The platform is in charge of providing power, attitude & orbit control and up/down-link services, whereas the payload represents the scientific and/or operational instruments/transponders and embodies the objectives of the mission. One major possibility to improve the performance of payloads, by limiting the data return to pertinent information, is to process data on board thanks to a proper implementation of the payload data system. In this way, it is possible to share non-recurring development costs by exploiting a system that can be adopted by the majority of space missions. It is believed that the Modular and Scalable Payload Data System, under development by ESA, provides a suitable solution to fulfil a large range of future mission requirements. The backbone of the system is the standardised high data rate SpaceWire network http://www.ecss.nl/. As complement, a lower speed command and control bus connecting peripherals is required. For instance, at instrument level, there is a need for a "local" low complexity bus, which gives the possibility to command and control sensors and actuators. Moreover, most of the connections at sub-system level are related to discrete signals management or simple telemetry acquisitions, which can easily and efficiently be handled by a local bus. An on-board hierarchical network can therefore be defined by interconnecting high-speed links and local buses. Additionally, it is worth stressing another important aspect of the design process: Agencies and ESA in particular are frequently confronted with a big consortium of geographically spread companies located in different countries, each one

  15. Hierarchical Reverberation Mapping

    CERN Document Server

    Brewer, Brendon J

    2013-01-01

    Reverberation mapping (RM) is an important technique in studies of active galactic nuclei (AGN). The key idea of RM is to measure the time lag $\\tau$ between variations in the continuum emission from the accretion disc and subsequent response of the broad line region (BLR). The measurement of $\\tau$ is typically used to estimate the physical size of the BLR and is combined with other measurements to estimate the black hole mass $M_{\\rm BH}$. A major difficulty with RM campaigns is the large amount of data needed to measure $\\tau$. Recently, Fine et al (2012) introduced a new approach to RM where the BLR light curve is sparsely sampled, but this is counteracted by observing a large sample of AGN, rather than a single system. The results are combined to infer properties of the sample of AGN. In this letter we implement this method using a hierarchical Bayesian model and contrast this with the results from the previous stacked cross-correlation technique. We find that our inferences are more precise and allow fo...

  16. Hierarchical Parallelization of Gene Differential Association Analysis

    Directory of Open Access Journals (Sweden)

    Dwarkadas Sandhya

    2011-09-01

    Full Text Available Abstract Background Microarray gene differential expression analysis is a widely used technique that deals with high dimensional data and is computationally intensive for permutation-based procedures. Microarray gene differential association analysis is even more computationally demanding and must take advantage of multicore computing technology, which is the driving force behind increasing compute power in recent years. In this paper, we present a two-layer hierarchical parallel implementation of gene differential association analysis. It takes advantage of both fine- and coarse-grain (with granularity defined by the frequency of communication parallelism in order to effectively leverage the non-uniform nature of parallel processing available in the cutting-edge systems of today. Results Our results show that this hierarchical strategy matches data sharing behavior to the properties of the underlying hardware, thereby reducing the memory and bandwidth needs of the application. The resulting improved efficiency reduces computation time and allows the gene differential association analysis code to scale its execution with the number of processors. The code and biological data used in this study are downloadable from http://www.urmc.rochester.edu/biostat/people/faculty/hu.cfm. Conclusions The performance sweet spot occurs when using a number of threads per MPI process that allows the working sets of the corresponding MPI processes running on the multicore to fit within the machine cache. Hence, we suggest that practitioners follow this principle in selecting the appropriate number of MPI processes and threads within each MPI process for their cluster configurations. We believe that the principles of this hierarchical approach to parallelization can be utilized in the parallelization of other computationally demanding kernels.

  17. Scale of association: hierarchical linear models and the measurement of ecological systems

    Science.gov (United States)

    Sean M. McMahon; Jeffrey M. Diez

    2007-01-01

    A fundamental challenge to understanding patterns in ecological systems lies in employing methods that can analyse, test and draw inference from measured associations between variables across scales. Hierarchical linear models (HLM) use advanced estimation algorithms to measure regression relationships and variance-covariance parameters in hierarchically structured...

  18. Modulated modularity clustering as an exploratory tool for functional genomic inference.

    Directory of Open Access Journals (Sweden)

    Eric A Stone

    2009-05-01

    Full Text Available In recent years, the advent of high-throughput assays, coupled with their diminishing cost, has facilitated a systems approach to biology. As a consequence, massive amounts of data are currently being generated, requiring efficient methodology aimed at the reduction of scale. Whole-genome transcriptional profiling is a standard component of systems-level analyses, and to reduce scale and improve inference clustering genes is common. Since clustering is often the first step toward generating hypotheses, cluster quality is critical. Conversely, because the validation of cluster-driven hypotheses is indirect, it is critical that quality clusters not be obtained by subjective means. In this paper, we present a new objective-based clustering method and demonstrate that it yields high-quality results. Our method, modulated modularity clustering (MMC, seeks community structure in graphical data. MMC modulates the connection strengths of edges in a weighted graph to maximize an objective function (called modularity that quantifies community structure. The result of this maximization is a clustering through which tightly-connected groups of vertices emerge. Our application is to systems genetics, and we quantitatively compare MMC both to the hierarchical clustering method most commonly employed and to three popular spectral clustering approaches. We further validate MMC through analyses of human and Drosophila melanogaster expression data, demonstrating that the clusters we obtain are biologically meaningful. We show MMC to be effective and suitable to applications of large scale. In light of these features, we advocate MMC as a standard tool for exploration and hypothesis generation.

  19. Cluster Decline and Resilience

    DEFF Research Database (Denmark)

    Østergaard, Christian Richter; Park, Eun Kyung

    -2011. Our longitudinal study reveals that technological lock-in and exit of key firms have contributed to impairment of the cluster’s resilience in adapting to disruptions. Entrepreneurship has a positive effect on cluster resilience, while multinational companies have contradicting effects by bringing......Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark, 1963...

  20. Topology of the correlation networks among major currencies using hierarchical structure methods

    Science.gov (United States)

    Keskin, Mustafa; Deviren, Bayram; Kocakaplan, Yusuf

    2011-02-01

    We studied the topology of correlation networks among 34 major currencies using the concept of a minimal spanning tree and hierarchical tree for the full years of 2007-2008 when major economic turbulence occurred. We used the USD (US Dollar) and the TL (Turkish Lira) as numeraires in which the USD was the major currency and the TL was the minor currency. We derived a hierarchical organization and constructed minimal spanning trees (MSTs) and hierarchical trees (HTs) for the full years of 2007, 2008 and for the 2007-2008 period. We performed a technique to associate a value of reliability to the links of MSTs and HTs by using bootstrap replicas of data. We also used the average linkage cluster analysis for obtaining the hierarchical trees in the case of the TL as the numeraire. These trees are useful tools for understanding and detecting the global structure, taxonomy and hierarchy in financial data. We illustrated how the minimal spanning trees and their related hierarchical trees developed over a period of time. From these trees we identified different clusters of currencies according to their proximity and economic ties. The clustered structure of the currencies and the key currency in each cluster were obtained and we found that the clusters matched nicely with the geographical regions of corresponding countries in the world such as Asia or Europe. As expected the key currencies were generally those showing major economic activity.

  1. Self-organized Criticality in Hierarchical Brain Network

    Institute of Scientific and Technical Information of China (English)

    YANG Qiu-Ying; ZHANG Ying-Yue; CHEN Tian-Lun

    2008-01-01

    It is shown that the cortical brain network of the macaque displays a hierarchically clustered organization and the neuron network shows small-world properties. Now the two factors will be considered in our model and the dynamical behavior of the model will be studied. We study the characters of the model and find that the distribution of avalanche size of the model follows power-law behavior.

  2. Hierarchical materials: Background and perspectives

    DEFF Research Database (Denmark)

    2016-01-01

    Hierarchical design draws inspiration from analysis of biological materials and has opened new possibilities for enhancing performance and enabling new functionalities and extraordinary properties. With the development of nanotechnology, the necessary technological requirements for the manufactur...

  3. Direct hierarchical assembly of nanoparticles

    Science.gov (United States)

    Xu, Ting; Zhao, Yue; Thorkelsson, Kari

    2014-07-22

    The present invention provides hierarchical assemblies of a block copolymer, a bifunctional linking compound and a nanoparticle. The block copolymers form one micro-domain and the nanoparticles another micro-domain.

  4. Bimodal Color Distribution in Hierarchical Galaxy Formation

    CERN Document Server

    Menci, N; Giallongo, E; Salimbeni, S

    2005-01-01

    We show how the observed bimodality in the color distribution of galaxies can be explained in the framework of the hierarchical clustering picture in terms of the interplay between the properties of the merging histories and the feedback/star-formation processes in the progenitors of local galaxies. Using a semi-analytic model of hierarchical galaxy formation, we compute the color distributions of galaxies with different luminosities and compare them with the observations. Our fiducial model matches the fundamental properties of the observed distributions, namely: 1) the distribution of objects brighter than M_r = -18 is clearly bimodal, with a fraction of red objects increasing with luminosity; 2) for objects brighter than M_r = -21 the color distribution is dominated by red objects with color u-r = 2.2-2.4; 3) the spread on the distribution of the red population is smaller than that of the blue population; 4) the fraction of red galaxies is larger in denser environments, even for low-luminosity objects; 5) ...

  5. Hierarchical video summarization for medical data

    Science.gov (United States)

    Zhu, Xingquan; Fan, Jianping; Elmagarmid, Ahmed K.; Aref, Walid G.

    2001-12-01

    To provide users with an overview of medical video content at various levels of abstraction which can be used for more efficient database browsing and access, a hierarchical video summarization strategy has been developed and is presented in this paper. To generate an overview, the key frames of a video are preprocessed to extract special frames (black frames, slides, clip art, sketch drawings) and special regions (faces, skin or blood-red areas). A shot grouping method is then applied to merge the spatially or temporally related shots into groups. The visual features and knowledge from the video shots are integrated to assign the groups into predefined semantic categories. Based on the video groups and their semantic categories, video summaries for different levels are constructed by group merging, hierarchical group clustering and semantic category selection. Based on this strategy, a user can select the layer of the summary to access. The higher the layer, the more concise the video summary; the lower the layer, the greater the detail contained in the summary.

  6. Multiple dynamical time-scales in networks with hierarchically nested modular organization

    Indian Academy of Sciences (India)

    Sitabhra Sinha; Swarup Poria

    2011-11-01

    Many natural and engineered complex networks have intricate mesoscopic organization, e.g., the clustering of the constituent nodes into several communities or modules. Often, such modularity is manifested at several different hierarchical levels, where the clusters defined at one level appear as elementary entities at the next higher level. Using a simple model of a hierarchical modular network, we show that such a topological structure gives rise to characteristic time-scale separation between dynamics occurring at different levels of the hierarchy. This generalizes our earlier result for simple modular networks, where fast intramodular and slow intermodular processes were clearly distinguished. Investigating the process of synchronization of oscillators in a hierarchical modular network, we show the existence of as many distinct time-scales as there are hierarchical levels in the system. This suggests a possible functional role of such mesoscopic organization principle in natural systems, viz., in the dynamical separation of events occurring at different spatial scales.

  7. PROPOSED A HETEROGENEOUS CLUSTERING ALGORITHM TO IMPROVE QOS IN WSN

    Directory of Open Access Journals (Sweden)

    Mehran Mokhtari

    2016-07-01

    Full Text Available In this article it has presented leach extended hierarchical 3-level clustered heterogeneous and dynamics algorithm. On suggested protocol (LEH3LA with planning of selected auction cluster head, and alternative cluster head node, problem of delay on processing, processing of selecting members, decrease of expenses, and energy consumption, decrease of sending message, and receiving messages inside the clusters, selecting of cluster heads in large sensor networks were solved. This algorithm uses hierarchical heterogeneous network (3-levels, collective intelligence, and intra-cluster interaction for communications. Also it will solve the problems of sending data in Multi-BS mobile networks, expanding inter-cluster networks, overlap cluster, genesis orphan nodes, boundary change dynamically clusters, using backbone networks, cloud sensor. Using sleep/wake scheduling algorithm or TDMA-schedule alternative cluster head node provides redundancy, and fault tolerance. Local processing in cluster head nodes, and alternative cluster head, intra-cluster and inter-cluster communications such as Multi-HOP cause increase on processing speed, and sending data intra-cluster and inter-cluster. Decrease of overhead network, and increase the load balancing among cluster heads. Using encapsulation of data method, by cluster head nodes, energy consumption decrease during sending data. Also by improving quality of service (QoS in CBRP, LEACH, 802.15.4, decrease of energy consumption in sensors, cluster heads and alternative cluster head nodes, cause increase on lift time of sensor networks

  8. Hierarchical method of task assignment for multiple cooperating UAV teams

    Institute of Scientific and Technical Information of China (English)

    Xiaoxuan Hu; Huawei Ma; Qingsong Ye; He Luo

    2015-01-01

    The problem of task assignment for multiple cooperat-ing unmanned aerial vehicle (UAV) teams is considered. Multiple UAVs forming several smal teams are needed to perform attack tasks on a set of predetermined ground targets. A hierarchical task assignment method is presented to address the problem. It breaks the original problem down to three levels of sub-problems: tar-get clustering, cluster al ocation and target assignment. The first two sub-problems are central y solved by using clustering algo-rithms and integer linear programming, respectively, and the third sub-problem is solved in a distributed and paral el manner, using a mixed integer linear programming model and an improved ant colony algorithm. The proposed hierarchical method can reduce the computational complexity of the task assignment problem con-siderably, especial y when the number of tasks or the number of UAVs is large. Experimental results show that this method is feasi-ble and more efficient than non-hierarchical methods.

  9. London Education and Inclusion Project (LEIP): Exploring Negative and Null Effects of a Cluster-Randomised School-Intervention to Reduce School Exclusion—Findings from Protocol-Based Subgroup Analyses

    Science.gov (United States)

    Obsuth, Ingrid; Cope, Aiden; Sutherland, Alex; Pilbeam, Liv; Murray, Aja Louise; Eisner, Manuel

    2016-01-01

    This paper presents subgroup analyses from the London Education and Inclusion Project (LEIP). LEIP was a cluster-randomised controlled trial of an intervention called Engage in Education-London (EiE-L) which aimed to reduce school exclusions in those at greatest risk of exclusion. Pupils in the control schools attended an hour-long employability seminar. Minimisation was used to randomly assign schools to treatment and control following baseline data collection. The study involved 36 schools (17 in treatment—373 pupils; 19 in control—369 pupils) with >28% free school meal eligibility across London and utilised on pupil self-reports, teacher reports as well as official records to assess the effectiveness of EiE-L. Due to multiple data sources, sample sizes varied according to analysis. Analyses of pre-specified subgroups revealed null and negative effects on school exclusion following the intervention. Our findings suggest that the design and implementation of EiE-L may have contributed to the negative outcomes for pupils in the treatment schools when compared to those in the control schools. These findings call into question the effectiveness of bolt-on short-term interventions with pupils, particularly those at the highest risk of school exclusion and when they are faced with multiple problems. This is especially pertinent given the possibility of negative outcomes. Trial Registration: Controlled Trials: ISRCTN23244695 PMID:27045953

  10. A Novel Cluster Head Selection Algorithm Based on Fuzzy Clustering and Particle Swarm Optimization.

    Science.gov (United States)

    Ni, Qingjian; Pan, Qianqian; Du, Huimin; Cao, Cen; Zhai, Yuqing

    2017-01-01

    An important objective of wireless sensor network is to prolong the network life cycle, and topology control is of great significance for extending the network life cycle. Based on previous work, for cluster head selection in hierarchical topology control, we propose a solution based on fuzzy clustering preprocessing and particle swarm optimization. More specifically, first, fuzzy clustering algorithm is used to initial clustering for sensor nodes according to geographical locations, where a sensor node belongs to a cluster with a determined probability, and the number of initial clusters is analyzed and discussed. Furthermore, the fitness function is designed considering both the energy consumption and distance factors of wireless sensor network. Finally, the cluster head nodes in hierarchical topology are determined based on the improved particle swarm optimization. Experimental results show that, compared with traditional methods, the proposed method achieved the purpose of reducing the mortality rate of nodes and extending the network life cycle.

  11. Hierarchical architecture of active knits

    Science.gov (United States)

    Abel, Julianna; Luntz, Jonathan; Brei, Diann

    2013-12-01

    Nature eloquently utilizes hierarchical structures to form the world around us. Applying the hierarchical architecture paradigm to smart materials can provide a basis for a new genre of actuators which produce complex actuation motions. One promising example of cellular architecture—active knits—provides complex three-dimensional distributed actuation motions with expanded operational performance through a hierarchically organized structure. The hierarchical structure arranges a single fiber of active material, such as shape memory alloys (SMAs), into a cellular network of interlacing adjacent loops according to a knitting grid. This paper defines a four-level hierarchical classification of knit structures: the basic knit loop, knit patterns, grid patterns, and restructured grids. Each level of the hierarchy provides increased architectural complexity, resulting in expanded kinematic actuation motions of active knits. The range of kinematic actuation motions are displayed through experimental examples of different SMA active knits. The results from this paper illustrate and classify the ways in which each level of the hierarchical knit architecture leverages the performance of the base smart material to generate unique actuation motions, providing necessary insight to best exploit this new actuation paradigm.

  12. Advanced hierarchical distance sampling

    Science.gov (United States)

    Royle, Andy

    2016-01-01

    In this chapter, we cover a number of important extensions of the basic hierarchical distance-sampling (HDS) framework from Chapter 8. First, we discuss the inclusion of “individual covariates,” such as group size, in the HDS model. This is important in many surveys where animals form natural groups that are the primary observation unit, with the size of the group expected to have some influence on detectability. We also discuss HDS integrated with time-removal and double-observer or capture-recapture sampling. These “combined protocols” can be formulated as HDS models with individual covariates, and thus they have a commonality with HDS models involving group structure (group size being just another individual covariate). We cover several varieties of open-population HDS models that accommodate population dynamics. On one end of the spectrum, we cover models that allow replicate distance sampling surveys within a year, which estimate abundance relative to availability and temporary emigration through time. We consider a robust design version of that model. We then consider models with explicit dynamics based on the Dail and Madsen (2011) model and the work of Sollmann et al. (2015). The final major theme of this chapter is relatively newly developed spatial distance sampling models that accommodate explicit models describing the spatial distribution of individuals known as Point Process models. We provide novel formulations of spatial DS and HDS models in this chapter, including implementations of those models in the unmarked package using a hack of the pcount function for N-mixture models.

  13. A Privacy Data-Oriented Hierarchical MapReduce Programming Model

    Directory of Open Access Journals (Sweden)

    Haiwen Han

    2013-08-01

    Full Text Available To realize privacy data protection efficiently in hybrid cloud service, a hierarchical control architecture based multi-cluster MapReduce programming model (the Hierarchical MapReduce Model,HMR is presented. Under this hierarchical control architecture,  data isolation and placement among private cloud and public clouds according to the data privacy characteristic is implemented by the control center in private cloud.  And then, to perform the corresponding distributed parallel computation correctly under the multi-clusters mode that is different to the conventional single-cluster mode, the Map-Reduce-GlobalReduce three stage scheduling process is designed. Limiting the computation about privacy data in private cloud while outsourcing the computation about non-privacy data to public clouds as much as possible, HMR reaches the performance of both security and low cost.  

  14. Etude de la magnétosphère terrestre par l'analyse multipoint des données de la mission CLUSTER. Contributions à la caractérisation des frontières et de la magnétosphère interne

    OpenAIRE

    Darrouzet, Fabien

    2006-01-01

    CLUSTER is the first space mission dedicated to the three-dimensional study of the terrestrial magnetosphere. Its polar orbit and four spacecraft tetrahedron formation allow it to make in situ measurements in various regions of the magnetosphere, in particular in the plasmasphere. This PhD thesis brings together several studies on plasma structures encountered by the CLUSTER spacecraft along their orbit, during the time period 2001-2004. The physical quantity analysed here is mainly the elect...

  15. Hierarchical topic modeling with nested hierarchical Dirichlet process

    Institute of Scientific and Technical Information of China (English)

    Yi-qun DING; Shan-ping LI; Zhen ZHANG; Bin SHEN

    2009-01-01

    This paper deals with the statistical modeling of latent topic hierarchies in text corpora. The height of the topic tree is assumed as fixed, while the number of topics on each level as unknown a priori and to be inferred from data. Taking a nonparametric Bayesian approach to this problem, we propose a new probabilistic generative model based on the nested hierarchical Dirichlet process (nHDP) and present a Markov chain Monte Carlo sampling algorithm for the inference of the topic tree structure as welt as the word distribution of each topic and topic distribution of each document. Our theoretical analysis and experiment results show that this model can produce a more compact hierarchical topic structure and captures more free-grained topic relationships compared to the hierarchical latent Dirichlet allocation model.

  16. Nonresident Undergraduates' Performance in English Writing Classes-Hierarchical Linear Modeling Analysis

    National Research Council Canada - National Science Library

    Allison A Vaughn; Matthew Bergman; Barry Fass-Holmes

    2015-01-01

    ...) in the fall term of the five most recent academic years. Hierarchical linear modeling analyses showed that the predictors with the largest effect sizes were English writing programs and class level...

  17. LIMO EEG: a toolbox for hierarchical LInear MOdeling of ElectroEncephaloGraphic data

    National Research Council Canada - National Science Library

    Pernet, Cyril R; Chauveau, Nicolas; Gaspar, Carl; Rousselet, Guillaume A

    2011-01-01

    ...). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data...

  18. LIMO EEG: A Toolbox for Hierarchical LInear MOdeling of ElectroEncephaloGraphic Data

    National Research Council Canada - National Science Library

    Pernet, Cyril R; Chauveau, Nicolas; Gaspar, Carl; Rousselet, Guillaume A

    2011-01-01

    ...). LIMO EEG is a Matlab toolbox (EEGLAB compatible) to analyse evoked responses over all space and time dimensions, while accounting for single trial variability using a simple hierarchical linear modelling of the data...

  19. Comparative analyses of fundamental differences in membrane transport capabilities in prokaryotes and eukaryotes.

    Directory of Open Access Journals (Sweden)

    Qinghu Ren

    2005-08-01

    Full Text Available Whole-genome transporter analyses have been conducted on 141 organisms whose complete genome sequences are available. For each organism, the complete set of membrane transport systems was identified with predicted functions, and classified into protein families based on the transporter classification system. Organisms with larger genome sizes generally possessed a relatively greater number of transport systems. In prokaryotes and unicellular eukaryotes, the significant factor in the increase in transporter content with genome size was a greater diversity of transporter types. In contrast, in multicellular eukaryotes, greater number of paralogs in specific transporter families was the more important factor in the increase in transporter content with genome size. Both eukaryotic and prokaryotic intracellular pathogens and endosymbionts exhibited markedly limited transport capabilities. Hierarchical clustering of phylogenetic profiles of transporter families, derived from the presence or absence of a certain transporter family, showed that clustering patterns of organisms were correlated to both their evolutionary history and their overall physiology and lifestyles.

  20. Comparative Analyses of Fundamental Differences in Membrane Transport Capabilities in Prokaryotes and Eukaryotes.

    Directory of Open Access Journals (Sweden)

    2005-08-01

    Full Text Available Whole-genome transporter analyses have been conducted on 141 organisms whose complete genome sequences are available. For each organism, the complete set of membrane transport systems was identified with predicted functions, and classified into protein families based on the transporter classification system. Organisms with larger genome sizes generally possessed a relatively greater number of transport systems. In prokaryotes and unicellular eukaryotes, the significant factor in the increase in transporter content with genome size was a greater diversity of transporter types. In contrast, in multicellular eukaryotes, greater number of paralogs in specific transporter families was the more important factor in the increase in transporter content with genome size. Both eukaryotic and prokaryotic intracellular pathogens and endosymbionts exhibited markedly limited transport capabilities. Hierarchical clustering of phylogenetic profiles of transporter families, derived from the presence or absence of a certain transporter family, showed that clustering patterns of organisms were correlated to both their evolutionary history and their overall physiology and lifestyles.

  1. 基于聚类分析的多尺度相似地震快速识别方法及其在汶川地震东北端余震序列分析中的应用%Quick identification of multilevel similar earthquakes using hierarchical clustering method and its application to Wenchuan northeast aftershock sequence

    Institute of Scientific and Technical Information of China (English)

    王伟涛; 王宝善

    2012-01-01

    相似地震是具有相似波形记录的一组地震,往往以地震丛集的方式发生,而重复地震是一种特殊的相似地震,一般具有相近的震源机制解和几乎重合的破裂面积.对相似地震特别是重复地震的研究是我们认识断层的结构和变化的重要手段.本文提出了一种基于相似度距离概念和聚类分析技术的相似地震识别方法,可以利用单个台站对其记录到的地震事件进行快速的相似地震和重复地震识别.我们将此方法应用于汶川地震东北端的余震序列,获得了该地区相似地震的分布图像,并对其中存在的重复地震的发震机制进行了讨论分析.%Similar earthquakes are a group of earthquakes which have highly similar waveform at one or more seismic stations, they always occur as clusters in a limited space. Repeating earthquakes, as distinguished similar earthquakes, have nearly identical focal mechanism and overlapped rupture area. They provide an important means for studying the structure and property variation of fault systems. Here we present a method, which is based on similarity distance matrix and hierarchical clustering algorithm, to perform multilevel quick identification of similar earthquakes by one single station. We apply this method to Wenchuan northeast aftershock sequence and obtain the spatial and time distribution of different level similar earthquakes. The ability for detecting repeating earthquakes as well as the possible mechanism of burst-type repeating earthquakes in this region are discussed in the end.

  2. Ways of looking ahead: hierarchical planning in language production.

    Science.gov (United States)

    Lee, Eun-Kyung; Brown-Schmidt, Sarah; Watson, Duane G

    2013-12-01

    It is generally assumed that language production proceeds incrementally, with chunks of linguistic structure planned ahead of speech. Extensive research has examined the scope of language production and suggests that the size of planned chunks varies across contexts (Ferreira & Swets, 2002; Wagner & Jescheniak, 2010). By contrast, relatively little is known about the structure of advance planning, specifically whether planning proceeds incrementally according to the surface structure of the utterance, or whether speakers plan according to the hierarchical relationships between utterance elements. In two experiments, we examine the structure and scope of lexical planning in language production using a picture description task. Analyses of speech onset times and word durations show that speakers engage in hierarchical planning such that structurally dependent lexical items are planned together and that hierarchical planning occurs for both direct and indirect dependencies. Copyright © 2013 Elsevier B.V. All rights reserved.

  3. Deliberate change without hierarchical influence?

    DEFF Research Database (Denmark)

    Nørskov, Sladjana; Kesting, Peter; Ulhøi, John Parm

    2017-01-01

    Purpose This paper aims to present that deliberate change is strongly associated with formal structures and top-down influence. Hierarchical configurations have been used to structure processes, overcome resistance and get things done. But is deliberate change also possible without formal...... reveals that deliberate change is indeed achievable in a non-hierarchical collaborative OSS community context. However, it presupposes the presence and active involvement of informal change agents. The paper identifies and specifies four key drivers for change agents’ influence. Originality....../value The findings contribute to organisational analysis by providing a deeper understanding of the importance of leadership in making deliberate change possible in non-hierarchical settings. It points to the importance of “change-by-conviction”, essentially based on voluntary behaviour. This can open the door...

  4. Static Correctness of Hierarchical Procedures

    DEFF Research Database (Denmark)

    Schwartzbach, Michael Ignatieff

    1990-01-01

    A system of hierarchical, fully recursive types in a truly imperative language allows program fragments written for small types to be reused for all larger types. To exploit this property to enable type-safe hierarchical procedures, it is necessary to impose a static requirement on procedure calls....... We introduce an example language and prove the existence of a sound requirement which preserves static correctness while allowing hierarchical procedures. This requirement is further shown to be optimal, in the sense that it imposes as few restrictions as possible. This establishes the theoretical...... basis for a general type hierarchy with static type checking, which enables first-order polymorphism combined with multiple inheritance and specialization in a language with assignments. We extend the results to include opaque types. An opaque version of a type is different from the original but has...

  5. Parallel hierarchical global illumination

    Energy Technology Data Exchange (ETDEWEB)

    Snell, Quinn O. [Iowa State Univ., Ames, IA (United States)

    1997-10-08

    Solving the global illumination problem is equivalent to determining the intensity of every wavelength of light in all directions at every point in a given scene. The complexity of the problem has led researchers to use approximation methods for solving the problem on serial computers. Rather than using an approximation method, such as backward ray tracing or radiosity, the authors have chosen to solve the Rendering Equation by direct simulation of light transport from the light sources. This paper presents an algorithm that solves the Rendering Equation to any desired accuracy, and can be run in parallel on distributed memory or shared memory computer systems with excellent scaling properties. It appears superior in both speed and physical correctness to recent published methods involving bidirectional ray tracing or hybrid treatments of diffuse and specular surfaces. Like progressive radiosity methods, it dynamically refines the geometry decomposition where required, but does so without the excessive storage requirements for ray histories. The algorithm, called Photon, produces a scene which converges to the global illumination solution. This amounts to a huge task for a 1997-vintage serial computer, but using the power of a parallel supercomputer significantly reduces the time required to generate a solution. Currently, Photon can be run on most parallel environments from a shared memory multiprocessor to a parallel supercomputer, as well as on clusters of heterogeneous workstations.

  6. Finite Population Correction for Two-Level Hierarchical Linear Models.

    Science.gov (United States)

    Lai, Mark H C; Kwok, Oi-Man; Hsiao, Yu-Yu; Cao, Qian

    2017-03-16

    The research literature has paid little attention to the issue of finite population at a higher level in hierarchical linear modeling. In this article, we propose a method to obtain finite-population-adjusted standard errors of Level-1 and Level-2 fixed effects in 2-level hierarchical linear models. When the finite population at Level-2 is incorrectly assumed as being infinite, the standard errors of the fixed effects are overestimated, resulting in lower statistical power and wider confidence intervals. The impact of ignoring finite population correction is illustrated by using both a real data example and a simulation study with a random intercept model and a random slope model. Simulation results indicated that the bias in the unadjusted fixed-effect standard errors was substantial when the Level-2 sample size exceeded 10% of the Level-2 population size; the bias increased with a larger intraclass correlation, a larger number of clusters, and a larger average cluster size. We also found that the proposed adjustment produced unbiased standard errors, particularly when the number of clusters was at least 30 and the average cluster size was at least 10. We encourage researchers to consider the characteristics of the target population for their studies and adjust for finite population when appropriate. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  7. Structural integrity of hierarchical composites

    Directory of Open Access Journals (Sweden)

    Marco Paggi

    2012-01-01

    Full Text Available Interface mechanical problems are of paramount importance in engineering and materials science. Traditionally, due to the complexity of modelling their mechanical behaviour, interfaces are often treated as defects and their features are not explored. In this study, a different approach is illustrated, where the interfaces play an active role in the design of innovative hierarchical composites and are fundamental for their structural integrity. Numerical examples regarding cutting tools made of hierarchical cellular polycrystalline materials are proposed, showing that tailoring of interface properties at the different scales is the way to achieve superior mechanical responses that cannot be obtained using standard materials

  8. Multilevel Techniques for the Clustering Problem

    Directory of Open Access Journals (Sweden)

    Noureddine Bouhmala

    2014-02-01

    Full Text Available Data Mining is concerned with the discovery of int eresting patterns and knowledge in data repositories. Cluster Analysis which belongs to the core methods of data mining is the process of discovering homogeneous groups called clusters. Given a data-set and some measure of similarity between data objects, the goal in most c lustering algorithms is maximizing both the homogeneity within each cluster and the heterogene ity between different clusters. In this work, two multilevel algorithms for the clustering problem are introduced. The multilevel paradigm suggests looking at the clustering proble m as a hierarchical optimization process going through different levels evolving from a coar se grain to fine grain strategy. The clustering problem is solved by first reducing the problem level by level to a coarser problem where an initial clustering is computed. The clustering of the coarser problem is mapped back level-by- level to obtain a better clustering of the original problem by refining the intermediate different clustering obtained at various levels. A benchmark using a number of data sets collected from a variety of domains is used to compare the effective ness of the hierarchical approach against its single-level counterpart.

  9. Conceptual hierarchical modeling to describe wetland plant community organization

    Science.gov (United States)

    Little, A.M.; Guntenspergen, G.R.; Allen, T.F.H.

    2010-01-01

    Using multivariate analysis, we created a hierarchical modeling process that describes how differently-scaled environmental factors interact to affect wetland-scale plant community organization in a system of small, isolated wetlands on Mount Desert Island, Maine. We followed the procedure: 1) delineate wetland groups using cluster analysis, 2) identify differently scaled environmental gradients using non-metric multidimensional scaling, 3) order gradient hierarchical levels according to spatiotem-poral scale of fluctuation, and 4) assemble hierarchical model using group relationships with ordination axes and post-hoc tests of environmental differences. Using this process, we determined 1) large wetland size and poor surface water chemistry led to the development of shrub fen wetland vegetation, 2) Sphagnum and water chemistry differences affected fen vs. marsh / sedge meadows status within small wetlands, and 3) small-scale hydrologic differences explained transitions between forested vs. non-forested and marsh vs. sedge meadow vegetation. This hierarchical modeling process can help explain how upper level contextual processes constrain biotic community response to lower-level environmental changes. It creates models with more nuanced spatiotemporal complexity than classification and regression tree procedures. Using this process, wetland scientists will be able to generate more generalizable theories of plant community organization, and useful management models. ?? Society of Wetland Scientists 2009.

  10. Hierarchical Ring Network Design Using Branch-and-Price

    DEFF Research Database (Denmark)

    Thomadsen, Tommy; Stidsen, Thomas K.

    2005-01-01

    We consider the problem of designing hierarchical two layer ring networks. The top layer consists of a federal-ring which establishes connection between a number of node disjoint metro-rings in a bottom layer. The objective is to minimize the costs of links in the network, taking both the fixed l...... for jointly solving the clustering problem, the metro ring design problem and the routing problem. Computational results are given for networks with up to 36 nodes.......We consider the problem of designing hierarchical two layer ring networks. The top layer consists of a federal-ring which establishes connection between a number of node disjoint metro-rings in a bottom layer. The objective is to minimize the costs of links in the network, taking both the fixed...... link establishment costs and the link capacity costs into account. Hierarchical ring network design problems combines the following optimization problems: Clustering, hub selection, metro ring design, federal ring design and routing problems. In this paper a branch-and-price algorithm is presented...

  11. Bayesian hierarchical grouping: Perceptual grouping as mixture estimation.

    Science.gov (United States)

    Froyen, Vicky; Feldman, Jacob; Singh, Manish

    2015-10-01

    We propose a novel framework for perceptual grouping based on the idea of mixture models, called Bayesian hierarchical grouping (BHG). In BHG, we assume that the configuration of image elements is generated by a mixture of distinct objects, each of which generates image elements according to some generative assumptions. Grouping, in this framework, means estimating the number and the parameters of the mixture components that generated the image, including estimating which image elements are "owned" by which objects. We present a tractable implementation of the framework, based on the hierarchical clustering approach of Heller and Ghahramani (2005). We illustrate it with examples drawn from a number of classical perceptual grouping problems, including dot clustering, contour integration, and part decomposition. Our approach yields an intuitive hierarchical representation of image elements, giving an explicit decomposition of the image into mixture components, along with estimates of the probability of various candidate decompositions. We show that BHG accounts well for a diverse range of empirical data drawn from the literature. Because BHG provides a principled quantification of the plausibility of grouping interpretations over a wide range of grouping problems, we argue that it provides an appealing unifying account of the elusive Gestalt notion of Prägnanz.

  12. A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

    Science.gov (United States)

    Chahine, Firas Safwan

    2012-01-01

    Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

  13. A Genetic Algorithm That Exchanges Neighboring Centers for Fuzzy c-Means Clustering

    Science.gov (United States)

    Chahine, Firas Safwan

    2012-01-01

    Clustering algorithms are widely used in pattern recognition and data mining applications. Due to their computational efficiency, partitional clustering algorithms are better suited for applications with large datasets than hierarchical clustering algorithms. K-means is among the most popular partitional clustering algorithm, but has a major…

  14. 基于层次聚类分析与数据图形化技术探讨 少腹逐瘀汤与温经汤的组方配伍特点%Explore the combination relationship of Shaofu Zhuyu Decoction and Wenjing Decoction based on hierarchical clustering analysis and data visualization technology

    Institute of Scientific and Technical Information of China (English)

    宿树兰; 叶亮; 尚尔鑫; 范欣生; 段金廒; 华永庆; 唐于平

    2011-01-01

    Objective: To explore the combination relationship of Shaofu Zhuyu Decoction (SFZYD) and Wenjing Decoction (WJD) based on the methods of hierarchical clustering analysis and data visualization in order to provide guidance for modern research of the formula. Methods: The data mining technology based on the hierarchical clustering analysis and data visualization was used to analysis the complicated correlations of Shaofu Zhuyu decoction and Wenjing Decoction after qualitative information of drugs property. Results: The analytic results stated that the drugs in SFZYD were classified four clusters of Danggui-Rougui-Xiaohuixiang-Ganjiang, Moyao-Yuanhu, Puhuang - Wulingzhi, and Chuanxiong-Chishao. WJD were classified four clusters of Shengjiang-Banxia-Wuzhuyu- Danggui. Renshen-Mandong-Gancao, Shaoyao, and Ajiao-Danpi-Guizhi-Chuanxiong. The graphic models of Xing, and Wei, and Guijing stated that the properties of drugs in SFZYD are mainly distribute in warm and hot scope, but the properties of drugs in WJD are distributed in two scopes; About the Guijing, the drugs of SFZYD fasten on Pi (Wei), Gan (Dan), Xin (Xiaochang), while the drugs of WJD fasten on Pi (Wei), Gan (Dan), Fei (Da chang), Shen (Pangguang). The Pi (Wei) and Gan (Dan) channels are the important distribution areas. The pungent, sweet and hard are the major taste. Conclusion: The results are agreeing with the theory of TCM and provide guidance for modern research of TCM formulae. The multi-mathematical analysis methods may be feasible for research the complex correlations.%目的:研究少腹逐瘀汤与温经汤的配伍特点,为治疗妇科瘀血腹痛提供治疗思路及理论依据.方法:对少腹逐瘀汤与温经汤组方药物的药性信息进行量化处理,采用层次聚类分析法、数据图形化技术分别对两方组方特点进行分析.结果:聚类分析结果表明:少腹逐瘀汤10味药物根据其性、味、归经属性聚类为当归、肉桂、小茴香与干姜;没药与

  15. Biomedical ontology improves biomedical literature clustering performance: a comparison study.

    Science.gov (United States)

    Yoo, Illhoi; Hu, Xiaohua; Song, Il-Yeol

    2007-01-01

    Document clustering has been used for better document retrieval and text mining. In this paper, we investigate if a biomedical ontology improves biomedical literature clustering performance in terms of the effectiveness and the scalability. For this investigation, we perform a comprehensive comparison study of various document clustering approaches such as hierarchical clustering methods, Bisecting K-means, K-means and Suffix Tree Clustering (STC). According to our experiment results, a biomedical ontology significantly enhances clustering quality on biomedical documents. In addition, our results show that decent document clustering approaches, such as Bisecting K-means, K-means and STC, gains some benefit from the ontology while hierarchical algorithms showing the poorest clustering quality do not reap the benefit of the biomedical ontology.

  16. A Survey of Clustering Approaches for Mobile Ad Hoc Network

    Directory of Open Access Journals (Sweden)

    Mihir Mehta

    2014-02-01

    Full Text Available In MANET, Clustering is the most significant research area now days. Clustering offers several advantages like it improves stability of network, enhances routing in network, efficient resource allocation among mobile nodes in network and hierarchical routing structure. This survey paper analyzes number of clustering approaches which are widely used for partitioning mobile nodes into different virtual groups. Each clustering algorithm considers different parameters for selection of Cluster Head in Cluster. Cluster Head election is invoked on demand and it is aimed to decrease the computation and communication cost in MANET. Each approach has its own pros and cons.

  17. Energy Aware Clustering Algorithms for Wireless Sensor Networks

    Science.gov (United States)

    Rakhshan, Noushin; Rafsanjani, Marjan Kuchaki; Liu, Chenglian

    2011-09-01

    The sensor nodes deployed in wireless sensor networks (WSNs) are extremely power constrained, so maximizing the lifetime of the entire networks is mainly considered in the design. In wireless sensor networks, hierarchical network structures have the advantage of providing scalable and energy efficient solutions. In this paper, we investigate different clustering algorithms for WSNs and also compare these clustering algorithms based on metrics such as clustering distribution, cluster's load balancing, Cluster Head's (CH) selection strategy, CH's role rotation, node mobility, clusters overlapping, intra-cluster communications, reliability, security and location awareness.

  18. Kvalitative analyser ..

    DEFF Research Database (Denmark)

    Boolsen, Merete Watt

    bogen forklarer de fundamentale trin i forskningsprocessen og applikerer dem på udvalgte kvalitative analyser: indholdsanalyse, Grounded Theory, argumentationsanalyse og diskursanalyse......bogen forklarer de fundamentale trin i forskningsprocessen og applikerer dem på udvalgte kvalitative analyser: indholdsanalyse, Grounded Theory, argumentationsanalyse og diskursanalyse...

  19. The Case for A Hierarchal System Model for Linux Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Seager, M; Gorda, B

    2009-06-05

    The computer industry today is no longer driven, as it was in the 40s, 50s and 60s, by High-performance computing requirements. Rather, HPC systems, especially Leadership class systems, sit on top of a pyramid investment mode. Figure 1 shows a representative pyramid investment model for systems hardware. At the base of the pyramid is the huge investment (order 10s of Billions of US Dollars per year) in semiconductor fabrication and process technologies. These costs, which are approximately doubling with every generation, are funded from investments multiple markets: enterprise, desktops, games, embedded and specialized devices. Over and above these base technology investments are investments for critical technology elements such as microprocessor, chipsets and memory ASIC components. Investments for these components are spread across the same markets as the base semiconductor processes investments. These second tier investments are approximately half the size of the lower level of the pyramid. The next technology investment layer up, tier 3, is more focused on scalable computing systems such as those needed for HPC and other markets. These tier 3 technology elements include networking (SAN, WAN and LAN), interconnects and large scalable SMP designs. Above these is tier 4 are relatively small investments necessary to build very large, scalable systems high-end or Leadership class systems. Primary among these are the specialized network designs of vertically integrated systems, etc.

  20. Sensory Hierarchical Organization and Reading.

    Science.gov (United States)

    Skapof, Jerome

    The purpose of this study was to judge the viability of an operational approach aimed at assessing response styles in reading using the hypothesis of sensory hierarchical organization. A sample of 103 middle-class children from a New York City public school, between the ages of five and seven, took part in a three phase experiment. Phase one…

  1. Memory Stacking in Hierarchical Networks.

    Science.gov (United States)

    Westö, Johan; May, Patrick J C; Tiitinen, Hannu

    2016-02-01

    Robust representations of sounds with a complex spectrotemporal structure are thought to emerge in hierarchically organized auditory cortex, but the computational advantage of this hierarchy remains unknown. Here, we used computational models to study how such hierarchical structures affect temporal binding in neural networks. We equipped individual units in different types of feedforward networks with local memory mechanisms storing recent inputs and observed how this affected the ability of the networks to process stimuli context dependently. Our findings illustrate that these local memories stack up in hierarchical structures and hence allow network units to exhibit selectivity to spectral sequences longer than the time spans of the local memories. We also illustrate that short-term synaptic plasticity is a potential local memory mechanism within the auditory cortex, and we show that it can bring robustness to context dependence against variation in the temporal rate of stimuli, while introducing nonlinearities to response profiles that are not well captured by standard linear spectrotemporal receptive field models. The results therefore indicate that short-term synaptic plasticity might provide hierarchically structured auditory cortex with computational capabilities important for robust representations of spectrotemporal patterns.

  2. Cluster headache

    Science.gov (United States)

    Histamine headache; Headache - histamine; Migrainous neuralgia; Headache - cluster; Horton's headache; Vascular headache - cluster ... A cluster headache begins as a severe, sudden headache. The headache commonly strikes 2 to 3 hours after you fall ...

  3. Cluster Forests

    CERN Document Server

    Yan, Donghui; Jordan, Michael I

    2011-01-01

    Inspired by Random Forests (RF) in the context of classification, we propose a new clustering ensemble method---Cluster Forests (CF). Geometrically, CF randomly probes a high-dimensional data cloud to obtain "good local clusterings" and then aggregates via spectral clustering to obtain cluster assignments for the whole dataset. The search for good local clusterings is guided by a cluster quality measure $\\kappa$. CF progressively improves each local clustering in a fashion that resembles the tree growth in RF. Empirical studies on several real-world datasets under two different performance metrics show that CF compares favorably to its competitors. Theoretical analysis shows that the $\\kappa$ criterion is shown to grow each local clustering in a desirable way---it is "noise-resistant." A closed-form expression is obtained for the mis-clustering rate of spectral clustering under a perturbation model, which yields new insights into some aspects of spectral clustering.

  4. The Young Cluster and Star Forming Region NGC 2264

    OpenAIRE

    Dahm, S. E.

    2008-01-01

    NGC 2264 is a young Galactic cluster and the dominant component of the Mon OB1 association lying approximately 760 pc distant within the local spiral arm. The cluster is hierarchically structured, with subclusters of suspected members spread across several parsecs. Associated with the cluster is an extensive molecular cloud complex spanning more than two degrees on the sky. Star formation is ongoing within the region as evidenced by the presence of numerous embedded clusters of protostars, mo...

  5. Cluster stability scores for microarray data in cancer studies

    OpenAIRE

    Ghosh Debashis; Smolkin Mark

    2003-01-01

    Abstract Background A potential benefit of profiling of tissue samples using microarrays is the generation of molecular fingerprints that will define subtypes of disease. Hierarchical clustering has been the primary analytical tool used to define disease subtypes from microarray experiments in cancer settings. Assessing cluster reliability poses a major complication in analyzing output from clustering procedures. While most work has focused on estimating the number of clusters in a dataset, t...

  6. Star Clusters

    OpenAIRE

    Gieles, M.

    1993-01-01

    Star clusters are observed in almost every galaxy. In this thesis we address several fundamental problems concerning the formation, evolution and disruption of star clusters. From observations of (young) star clusters in the interacting galaxy M51, we found that clusters are formed in complexes of stars and star clusters. These complexes share similar properties with giant molecular clouds, from which they are formed. Many (70%) of the young clusters will not survive the fist 10 Myr, due to t...

  7. CLEAN: CLustering Enrichment ANalysis

    Directory of Open Access Journals (Sweden)

    Medvedovic Mario

    2009-07-01

    Full Text Available Abstract Background Integration of biological knowledge encoded in various lists of functionally related genes has become one of the most important aspects of analyzing genome-wide functional genomics data. In the context of cluster analysis, functional coherence of clusters established through such analyses have been used to identify biologically meaningful clusters, compare clustering algorithms and identify biological pathways associated with the biological process under investigation. Results We developed a computational framework for analytically and visually integrating knowledge-based functional categories with the cluster analysis of genomics data. The framework is based on the simple, conceptually appealing, and biologically interpretable gene-specific functional coherence score (CLEAN score. The score is derived by correlating the clustering structure as a whole with functional categories of interest. We directly demonstrate that integrating biological knowledge in this way improves the reproducibility of conclusions derived from cluster analysis. The CLEAN score differentiates between the levels of functional coherence for genes within the same cluster based on their membership in enriched functional categories. We show that this aspect results in higher reproducibility across independent datasets and produces more informative genes for distinguishing different sample types than the scores based on the traditional cluster-wide analysis. We also demonstrate the utility of the CLEAN framework in comparing clusterings produced by different algorithms. CLEAN was implemented as an add-on R package and can be downloaded at http://Clusteranalysis.org. The package integrates routines for calculating gene specific functional coherence scores and the open source interactive Java-based viewer Functional TreeView (FTreeView. Conclusion Our results indicate that using the gene-specific functional coherence score improves the reproducibility of the

  8. 新疆春小麦品种品质性状主成分及聚类分析%Principal Components and Cluster Analyses of Xinjiang Spring Wheat Quality Traits

    Institute of Scientific and Technical Information of China (English)

    高欢欢; 李卫华; 穆培源; 桑伟; 冶婷; 王亮

    2013-01-01

    [目的]选择不同筋力新疆自育和引进的春小麦品种,分析其磨粉品质、蛋白质品质性状和淀粉品质性状等指标,为育种家进一步开展品种选育和品质改良提供参考依据.[方法]选择30个春小麦品种,分析23个品种性状指标,采用主成分分析.把23个品质性状归于8个主成分,分别是淀粉糊化特性、粉质参数、蛋白质含量、降落数值、直链淀粉含量、面筋指数、峰值时间和Zeleny沉淀值,以品质因子进行聚类分析.[结果]经主成分分析,8个主成分其分别决定总变异量的44.00%、14.49%、8.52%、8.25%、5.46%、4.07%、3.30%和2.74%.把30个春小麦品种聚成4类:第一类小麦品种的面粉L*、面团形成时间、稳定时间和评价值等粉质参数上表现突出,面筋指数和淀粉糊化特性表现较好,而籽粒蛋白含量、湿面筋含量和面粉吸水率偏低.第二类小麦品种中的淀粉糊化特性好,湿面筋含量、籽粒蛋白质、面粉b*含量高,但面筋指数含量低.第三类小麦品种在面粉L*、面团稳定时间和评价值表现差.第四类小麦品种的灰分含量、面粉吸水率偏高,而形成时间较短,淀粉糊化特性表现较差.[结论]通过对新疆自育和引进的30个春小麦品种的23个品质性状的主成分和聚类分类分析,筛选出了影响春小麦品质的8个主成分,对这8个品质指标的选择有利于小麦品质育种和改良效率的提高.%[ Objective ] Gluten spring wheat of 30 different self - fertile or introduced varieties in Xinjiang were chosen to conduct principal component and cluster analyses of 23 indexes such as their flour quality, protein quality traits and starch quality traits. [Method] By the principal component analysis, the 23 quality characters belong to 8 principal components; starch gelatinization characteristics, silty parameters, protein content, falling number, amylose content, gluten index, peak time and Zeleny

  9. References for Galaxy Clusters Database

    OpenAIRE

    Kalinkov, M.; Valtchanov, I.; Kuneva, I.

    1998-01-01

    A bibliographic database will be constructed with the purpose to be a general tool for searching references for galaxy clusters. The structure of the database will be completely different from the available now databases as NED, SIMBAD, LEDA. Search based on hierarchical keyword system will be performed through web interfaces from numerous bibliographic sources -- journal articles, preprints, unpublished results and papers, theses, scientific reports. Data from the very beginning of the extra...

  10. Sentence Clustering Using Parts-of-Speech

    Directory of Open Access Journals (Sweden)

    Richard Khoury

    2012-02-01

    Full Text Available Clustering algorithms are used in many Natural Language Processing (NLP tasks. They have proven to be popular and effective tools to use to discover groups of similar linguistic items. In this exploratory paper, we propose a new clustering algorithm to automatically cluster together similar sentences based on the sentences’ part-of-speech syntax. The algorithm generates and merges together the clusters using a syntactic similarity metric based on a hierarchical organization of the parts-of-speech. We demonstrate the features of this algorithm by implementing it in a question type classification system, in order to determine the positive or negative impact of different changes to the algorithm.

  11. Synthesis of New Dynamic Movement Primitives Through Search in a Hierarchical Database of Example Movements

    Directory of Open Access Journals (Sweden)

    Miha Deniša

    2015-10-01

    Full Text Available This paper presents a novel approach to discovering motor primitives in a hierarchical database of example trajectories. An initial set of example trajectories is obtained by human demonstration. The trajectories are clustered and organized in a binary tree-like hierarchical structure, from which transition graphs at different levels of granularity are constructed. A novel procedure for searching in this hierarchical structure is presented. It can exploit the interdependencies between movements and can discover new series of partial paths. From these partial paths, complete new movements are generated by encoding them as dynamic movement primitives. In this way, the number of example trajectories that must be acquired with the assistance of a human teacher can be reduced. By combining the results of the hierarchical search with statistical generalization techniques, a complete representation of new, not directly demonstrated, movement primitives can be generated.

  12. Spectral characterization of hierarchical network modularity and limits of modularity detection.

    Directory of Open Access Journals (Sweden)

    Somwrita Sarkar

    Full Text Available Many real world networks are reported to have hierarchically modular organization. However, there exists no algorithm-independent metric to characterize hierarchical modularity in a complex system. The main results of the paper are a set of methods to address this problem. First, classical results from random matrix theory are used to derive the spectrum of a typical stochastic block model hierarchical modular network form. Second, it is shown that hierarchical modularity can be fingerprinted using the spectrum of its largest eigenvalues and gaps between clusters of closely spaced eigenvalues that are well separated from the bulk distribution of eigenvalues around the origin. Third, some well-known results on fingerprinting non-hierarchical modularity in networks automatically follow as special cases, threreby unifying these previously fragmented results. Finally, using these spectral results, it is found that the limits of detection of modularity can be empirically established by studying the mean values of the largest eigenvalues and the limits of the bulk distribution of eigenvalues for an ensemble of networks. It is shown that even when modularity and hierarchical modularity are present in a weak form in the network, they are impossible to detect, because some of the leading eigenvalues fall within the bulk distribution. This provides a threshold for the detection of modularity. Eigenvalue distributions of some technological, social, and biological networks are studied, and the implications of detecting hierarchical modularity in real world networks are discussed.

  13. Hierarchical Prisoner's Dilemma in Hierarchical Public-Goods Game

    CERN Document Server

    Fujimoto, Yuma; Kaneko, Kunihiko

    2016-01-01

    The dilemma in cooperation is one of the major concerns in game theory. In a public-goods game, each individual pays a cost for cooperation, or to prevent defection, and receives a reward from the collected cost in a group. Thus, defection is beneficial for each individual, while cooperation is beneficial for the group. Now, groups (say, countries) consisting of individual players also play games. To study such a multi-level game, we introduce a hierarchical public-goods (HPG) game in which two groups compete for finite resources by utilizing costs collected from individuals in each group. Analyzing this HPG game, we found a hierarchical prisoner's dilemma, in which groups choose the defection policy (say, armaments) as a Nash strategy to optimize each group's benefit, while cooperation optimizes the total benefit. On the other hand, for each individual within a group, refusing to pay the cost (say, tax) is a Nash strategy, which turns to be a cooperation policy for the group, thus leading to a hierarchical d...

  14. Interpolation based consensus clustering for gene expression time series.

    Science.gov (United States)

    Chiu, Tai-Yu; Hsu, Ting-Chieh; Yen, Chia-Cheng; Wang, Jia-Shung

    2015-04-16

    Unsupervised analyses such as clustering are the essential tools required to interpret time-series expression data from microarrays. Several clustering algorithms have been developed to analyze gene expression data. Early methods such as k-means, hierarchical clustering, and self-organizing maps are popular for their simplicity. However, because of noise and uncertainty of measurement, these common algorithms have low accuracy. Moreover, because gene expression is a temporal process, the relationship between successive time points should be considered in the analyses. In addition, biological processes are generally continuous; therefore, the datasets collected from time series experiments are often found to have an insufficient number of data points and, as a result, compensation for missing data can also be an issue. An affinity propagation-based clustering algorithm for time-series gene expression data is proposed. The algorithm explores the relationship between genes using a sliding-window mechanism to extract a large number of features. In addition, the time-course datasets are resampled with spline interpolation to predict the unobserved values. Finally, a consensus process is applied to enhance the robustness of the method. Some real gene expression datasets were analyzed to demonstrate the accuracy and efficiency of the algorithm. The proposed algorithm has benefitted from the use of cubic B-splines interpolation, sliding-window, affinity propagation, gene relativity graph, and a consensus process, and, as a result, provides both appropriate and effective clustering of time-series gene expression data. The proposed method was tested with gene expression data from the Yeast galactose dataset, the Yeast cell-cycle dataset (Y5), and the Yeast sporulation dataset, and the results illustrated the relationships between the expressed genes, which may give some insights into the biological processes involved.

  15. SOFM Neural Network Based Hierarchical Topology Control for Wireless Sensor Networks

    OpenAIRE

    2014-01-01

    Well-designed network topology provides vital support for routing, data fusion, and target tracking in wireless sensor networks (WSNs). Self-organization feature map (SOFM) neural network is a major branch of artificial neural networks, which has self-organizing and self-learning features. In this paper, we propose a cluster-based topology control algorithm for WSNs, named SOFMHTC, which uses SOFM neural network to form a hierarchical network structure, completes cluster head selection by the...

  16. Intrusion Detection Method Based on Improved Growing Hierarchical Self-Organizing Map

    Institute of Scientific and Technical Information of China (English)

    张亚平; 布文秀; 苏畅; 王璐瑶; 许涵

    2016-01-01

    Considering that growing hierarchical self-organizing map(GHSOM) ignores the influence of individ-ual component in sample vector analysis, and its accurate rate in detecting unknown network attacks is relatively lower, an improved GHSOM method combined with mutual information is proposed. After theoretical analysis, experiments are conducted to illustrate the effectiveness of the proposed method by accurately clustering the input data. Based on different clusters, the complex relationship within the data can be revealed effectively.

  17. Cities and regions in Britain through hierarchical percolation

    Science.gov (United States)

    Arcaute, Elsa; Molinero, Carlos; Hatna, Erez; Murcio, Roberto; Vargas-Ruiz, Camilo; Masucci, A. Paolo; Batty, Michael

    2016-04-01

    Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations which are the outcome of complex geographical, political and historical processes which leave almost indelible footprints on infrastructure such as the street network. In this work, we uncover a set of hierarchies in Britain at different scales using percolation theory on the street network and on its intersections which are the primary points of interaction and urban agglomeration. At the larger scales, the observed hierarchical structures can be interpreted as regional fractures of Britain, observed in various forms, from natural boundaries, such as National Parks, to regional divisions based on social class and wealth such as the well-known North-South divide. At smaller scales, cities are generated through recursive percolations on each of the emerging regional clusters. We examine the evolution of the morphology of the system as a whole, by measuring the fractal dimension of the clusters at each distance threshold in the percolation. We observe that this reaches a maximum plateau at a specific distance. The clusters defined at this distance threshold are in excellent correspondence with the boundaries of cities recovered from satellite images, and from previous methods using population density.

  18. Cities and regions in Britain through hierarchical percolation

    Science.gov (United States)

    Arcaute, Elsa; Molinero, Carlos; Hatna, Erez; Murcio, Roberto; Vargas-Ruiz, Camilo; Masucci, A. Paolo; Batty, Michael

    2016-01-01

    Urban systems present hierarchical structures at many different scales. These are observed as administrative regional delimitations which are the outcome of complex geographical, political and historical processes which leave almost indelible footprints on infrastructure such as the street network. In this work, we uncover a set of hierarchies in Britain at different scales using percolation theory on the street network and on its intersections which are the primary points of interaction and urban agglomeration. At the larger scales, the observed hierarchical structures can be interpreted as regional fractures of Britain, observed in various forms, from natural boundaries, such as National Parks, to regional divisions based on social class and wealth such as the well-known North–South divide. At smaller scales, cities are generated through recursive percolations on each of the emerging regional clusters. We examine the evolution of the morphology of the system as a whole, by measuring the fractal dimension of the clusters at each distance threshold in the percolation. We observe that this reaches a maximum plateau at a specific distance. The clusters defined at this distance threshold are in excellent correspondence with the boundaries of cities recovered from satellite images, and from previous methods using population density. PMID:27152211

  19. Categorization of gait patterns in adults with cerebral palsy: a clustering approach.

    Science.gov (United States)

    Roche, Nicolas; Pradon, Didier; Cosson, Julie; Robertson, Johanna; Marchiori, Claire; Zory, Raphael

    2014-01-01

    Gait patterns in adults with cerebral palsy have, to our knowledge, never been assessed. This contrasts with the large number of studies which have attempted to categorize gait patterns in children with cerebral palsy. Several methodological approaches have been developed to objectively classify gait patterns in patients with central nervous system lesions. These methods enable the identification of groups of patients with common underlying clinical problems. One method is cluster analysis, a multivariate statistical method which is used to classify an entire data set into homogeneous groups or "clusters". The aim of this study was to determine, using cluster analysis, the principal gait patterns which can be found in adults with cerebral palsy. Data from 3D motion analyses of 44 adults with cerebral palsy were included. A hierarchical cluster analysis was used to subgroup the different gait patterns based on spatiotemporal and kinematic parameters in the sagittal and frontal planes. Five clusters were identified (C1-C5) among which, 3 subgroups were determined, based on spontaneous gait speed (C1/C2: slow, C3/C4: moderate and C5: almost normal). The different clusters were related to specific kinematic parameters that can be assessed in routine clinical practice. These 5 classifications can be used to follow changes in gait patterns throughout growth and aging as well to assess the effects of different treatments (physiotherapy, surgery, botulinum toxin, etc.) on gait patterns in adults with cerebral palsy.

  20. The method of parallel-hierarchical transformation for rapid recognition of dynamic images using GPGPU technology

    Science.gov (United States)

    Timchenko, Leonid; Yarovyi, Andrii; Kokriatskaya, Nataliya; Nakonechna, Svitlana; Abramenko, Ludmila; Ławicki, Tomasz; Popiel, Piotr; Yesmakhanova, Laura

    2016-09-01

    The paper presents a method of parallel-hierarchical transformations for rapid recognition of dynamic images using GPU technology. Direct parallel-hierarchical transformations based on cluster CPU-and GPU-oriented hardware platform. Mathematic models of training of the parallel hierarchical (PH) network for the transformation are developed, as well as a training method of the PH network for recognition of dynamic images. This research is most topical for problems on organizing high-performance computations of super large arrays of information designed to implement multi-stage sensing and processing as well as compaction and recognition of data in the informational structures and computer devices. This method has such advantages as high performance through the use of recent advances in parallelization, possibility to work with images of ultra dimension, ease of scaling in case of changing the number of nodes in the cluster, auto scan of local network to detect compute nodes.

  1. Parallel Implementation of the Recursive Approximation of an Unsupervised Hierarchical Segmentation Algorithm. Chapter 5

    Science.gov (United States)

    Tilton, James C.; Plaza, Antonio J. (Editor); Chang, Chein-I. (Editor)

    2008-01-01

    The hierarchical image segmentation algorithm (referred to as HSEG) is a hybrid of hierarchical step-wise optimization (HSWO) and constrained spectral clustering that produces a hierarchical set of image segmentations. HSWO is an iterative approach to region grooving segmentation in which the optimal image segmentation is found at N(sub R) regions, given a segmentation at N(sub R+1) regions. HSEG's addition of constrained spectral clustering makes it a computationally intensive algorithm, for all but, the smallest of images. To counteract this, a computationally efficient recursive approximation of HSEG (called RHSEG) has been devised. Further improvements in processing speed are obtained through a parallel implementation of RHSEG. This chapter describes this parallel implementation and demonstrates its computational efficiency on a Landsat Thematic Mapper test scene.

  2. Ee-Leach(Low Energy Adaptive Clustering Hierarchy Modified Protocol

    Directory of Open Access Journals (Sweden)

    Nishita Payar,

    2014-05-01

    Full Text Available A wireless sensor network is made by many homogeneous and/or nodes which can sense data and communicate to each other. As energy is a scarce resource in WSN, the main issue is energy efficient routing. Many flat and hierarchical protocols have been projected to enhance the network lifetime. Low Energy Adaptive Clustering Hierarchy (LEACH protocol is a basic energy efficient hierarchical routing protocol in WSN. In LEACH, cluster heads are selected and cluster is formed by joining non cluster head nodes. Member nodes transmit the data to respective cluster head and the cluster head is conscientious to transmit the gathered and aggregated data directly to the base station. This paper examines the performance of the conventional LEACH protocol and gives an enhancement to it for energy efficiency. The proposed protocol considers many parameters like residual energy and distance from base station etc. for cluster head selection and energy efficient routing.

  3. Hierarchical structure of biological systems

    Science.gov (United States)

    Alcocer-Cuarón, Carlos; Rivera, Ana L; Castaño, Victor M

    2014-01-01

    A general theory of biological systems, based on few fundamental propositions, allows a generalization of both Wierner and Berthalanffy approaches to theoretical biology. Here, a biological system is defined as a set of self-organized, differentiated elements that interact pair-wise through various networks and media, isolated from other sets by boundaries. Their relation to other systems can be described as a closed loop in a steady-state, which leads to a hierarchical structure and functioning of the biological system. Our thermodynamical approach of hierarchical character can be applied to biological systems of varying sizes through some general principles, based on the exchange of energy information and/or mass from and within the systems. PMID:24145961

  4. Automatic Hierarchical Color Image Classification

    Directory of Open Access Journals (Sweden)

    Jing Huang

    2003-02-01

    Full Text Available Organizing images into semantic categories can be extremely useful for content-based image retrieval and image annotation. Grouping images into semantic classes is a difficult problem, however. Image classification attempts to solve this hard problem by using low-level image features. In this paper, we propose a method for hierarchical classification of images via supervised learning. This scheme relies on using a good low-level feature and subsequently performing feature-space reconfiguration using singular value decomposition to reduce noise and dimensionality. We use the training data to obtain a hierarchical classification tree that can be used to categorize new images. Our experimental results suggest that this scheme not only performs better than standard nearest-neighbor techniques, but also has both storage and computational advantages.

  5. Hierarchical matrices algorithms and analysis

    CERN Document Server

    Hackbusch, Wolfgang

    2015-01-01

    This self-contained monograph presents matrix algorithms and their analysis. The new technique enables not only the solution of linear systems but also the approximation of matrix functions, e.g., the matrix exponential. Other applications include the solution of matrix equations, e.g., the Lyapunov or Riccati equation. The required mathematical background can be found in the appendix. The numerical treatment of fully populated large-scale matrices is usually rather costly. However, the technique of hierarchical matrices makes it possible to store matrices and to perform matrix operations approximately with almost linear cost and a controllable degree of approximation error. For important classes of matrices, the computational cost increases only logarithmically with the approximation error. The operations provided include the matrix inversion and LU decomposition. Since large-scale linear algebra problems are standard in scientific computing, the subject of hierarchical matrices is of interest to scientists ...

  6. Distance function selection in several clustering algorithrms

    Institute of Scientific and Technical Information of China (English)

    LU Yu

    2004-01-01

    Most clustering algorithms need to describe the similarity of objects by a predefined distance function. Three distance functions which are widely used in two traditional clustering algorithms k-means and hierarchical clustering were investigated.Both theoretical analysis and detailed experimental results were given. It is shown that a distance function greatly affects clustering results and can be used to detect the outlier of a cluster by the comparison of such different results and give the shape information of clusters. In practice situation, it is suggested to use different distance function separately, compare the clustering results and pick out the "swing points". And such points may leak out more information for data analysts.

  7. Intraclass Correlation Coefficients in Hierarchical Designs: Evaluation Using Latent Variable Modeling

    Science.gov (United States)

    Raykov, Tenko

    2011-01-01

    Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…

  8. The Hierarchical Trend Model for property valuation and local price indices

    NARCIS (Netherlands)

    M.K. Francke; G.A. Vos

    2002-01-01

    This paper presents a hierarchical trend model (HTM) for selling prices of houses, addressing three main problems: the spatial and temporal dependence of selling prices and the dependency of price index changes on housing quality. In this model the general price trend, cluster-level price trends, an

  9. Gene Expression Data Knowledge Discovery using Global and Local Clustering

    CERN Document Server

    H, Swathi

    2010-01-01

    To understand complex biological systems, the research community has produced huge corpus of gene expression data. A large number of clustering approaches have been proposed for the analysis of gene expression data. However, extracting important biological knowledge is still harder. To address this task, clustering techniques are used. In this paper, hybrid Hierarchical k-Means algorithm is used for clustering and biclustering gene expression data is used. To discover both local and global clustering structure biclustering and clustering algorithms are utilized. A validation technique, Figure of Merit is used to determine the quality of clustering results. Appropriate knowledge is mined from the clusters by embedding a BLAST similarity search program into the clustering and biclustering process. To discover both local and global clustering structure biclustering and clustering algorithms are utilized. To determine the quality of clustering results, a validation technique, Figure of Merit is used. Appropriate ...

  10. Hybrid and hierarchical composite materials

    CERN Document Server

    Kim, Chang-Soo; Sano, Tomoko

    2015-01-01

    This book addresses a broad spectrum of areas in both hybrid materials and hierarchical composites, including recent development of processing technologies, structural designs, modern computer simulation techniques, and the relationships between the processing-structure-property-performance. Each topic is introduced at length with numerous  and detailed examples and over 150 illustrations.   In addition, the authors present a method of categorizing these materials, so that representative examples of all material classes are discussed.

  11. Measuring efficiency of a hierarchical organization with fuzzy DEA method

    OpenAIRE

    LUBAN Florica

    2009-01-01

    The paper analyses how the data envelopment analysis (DEA) and fuzzy set theory can be used to measure and evaluate the efficiency of a hierarchical system with n decision making units and a coordinating unit. It is presented a model for determining the of activity levels of decision making units so as to achieve both fuzzy objectives of achieving global target levels of coordination unit on the inputs and outputs and individual target levels of decision making units, and then some methods to...

  12. Treatment Protocols as Hierarchical Structures

    Science.gov (United States)

    Ben-Bassat, Moshe; Carlson, Richard W.; Puri, Vinod K.; Weil, Max Harry

    1978-01-01

    We view a treatment protocol as a hierarchical structure of therapeutic modules. The lowest level of this structure consists of individual therapeutic actions. Combinations of individual actions define higher level modules, which we call routines. Routines are designed to manage limited clinical problems, such as the routine for fluid loading to correct hypovolemia. Combinations of routines and additional actions, together with comments, questions, or precautions organized in a branching logic, in turn, define the treatment protocol for a given disorder. Adoption of this modular approach may facilitate the formulation of treatment protocols, since the physician is not required to prepare complex flowcharts. This hierarchical approach also allows protocols to be updated and modified in a flexible manner. By use of such a standard format, individual components may be fitted together to create protocols for multiple disorders. The technique is suited for computer implementation. We believe that this hierarchical approach may facilitate standarization of patient care as well as aid in clinical teaching. A protocol for acute pancreatitis is used to illustrate this technique.

  13. Hierarchical mechanisms of spatially contagious seed dispersal in complex seed-disperser networks.

    Science.gov (United States)

    Fedriani, José M; Wiegand, Thorsten

    2014-02-01

    Intra- and interspecific spatially contagious seed dispersal has far-reaching implications for plant recruitment, distribution, and community assemblage. However, logistical and analytical limitations have curtailed our understanding concerning the mechanisms and resulting spatial patterns of contagious seed dispersal in most systems and, especially, in complex seed-disperser networks. We investigated mechanisms of seed aggregation using techniques of spatial point pattern analysis and extensive data sets on mutispecific endozoochorous seed rain generated by five frugivorous mammals in three Mediterranean shrublands over two seasons. Our novel analytical approach revealed three hierarchical and complementary mechanisms of seed aggregation acting at different levels (fecal samples, seeds, pairs of seed species) and spatial scales. First, the three local guilds of frugivores tended to deliver their feces highly aggregated at small and intermediate spatial scales, and the overall pattern of fecal delivery could be described well by a nested double-cluster Thomas process. Second, once the strong observed fecal aggregation was accounted for, the distribution of mammal feces containing seeds was clustered within the pattern of all feces (i.e., with and without seeds), and the density of fecal samples containing seeds was higher than expected around other feces containing seeds in two out of the three studied seed-disperser networks. Finally, at a finer level, mark correlation analyses revealed that for some plant species pairs, the number of dispersed seeds was positively associated either at small or large spatial scales. Despite the relatively invariant patterning of nested double-clustering, some attributes of endozoochorous seed rain (e.g., intensity, scales of aggregation) were variable among study sites due to changes in the ecological context in which seeds and their dispersers interact. Our investigation disentangles for the first time the hierarchy of synergic

  14. An Extended Hierarchical Trusted Model for Wireless Sensor Networks

    Institute of Scientific and Technical Information of China (English)

    DU Ruiying; XU Mingdi; ZHANG Huanguo

    2006-01-01

    Cryptography and authentication are traditional approach for providing network security. However, they are not sufficient for solving the problems which malicious nodes compromise whole wireless sensor network leading to invalid data transmission and wasting resource by using vicious behaviors. This paper puts forward an extended hierarchical trusted architecture for wireless sensor network, and establishes trusted congregations by three-tier framework. The method combines statistics, economics with encrypt mechanism for developing two trusted models which evaluate cluster head nodes and common sensor nodes respectively. The models form logical trusted-link from command node to common sensor nodes and guarantees the network can run in secure and reliable circumstance.

  15. Modeling Formation of Globular Clusters: Beacons of Galactic Star Formation

    CERN Document Server

    Gnedin, Oleg Y

    2010-01-01

    Modern hydrodynamic simulations of galaxy formation are able to predict accurately the rates and locations of the assembly of giant molecular clouds in early galaxies. These clouds could host star clusters with the masses and sizes of real globular clusters. I describe current state-of-the-art simulations aimed at understanding the origin of the cluster mass function and metallicity distribution. Metallicity bimodality of globular cluster systems appears to be a natural outcome of hierarchical formation and gradually declining fraction of cold gas in galaxies. Globular cluster formation was most prominent at redshifts z>3, when massive star clusters may have contributed as much as 20% of all galactic star formation.

  16. The hierarchical brain network for face recognition.

    Science.gov (United States)

    Zhen, Zonglei; Fang, Huizhen; Liu, Jia

    2013-01-01

    Numerous functional magnetic resonance imaging (fMRI) studies have identified multiple cortical regions that are involved in face processing in the human brain. However, few studies have characterized the face-processing network as a functioning whole. In this study, we used fMRI to identify face-selective regions in the entire brain and then explore the hierarchical structure of the face-processing network by analyzing functional connectivity among these regions. We identified twenty-five regions mainly in the occipital, temporal and frontal cortex that showed a reliable response selective to faces (versus objects) across participants and across scan sessions. Furthermore, these regions were clustered into three relatively independent sub-networks in a face-recognition task on the basis of the strength of functional connectivity among them. The functionality of the sub-networks likely corresponds to the recognition of individual identity, retrieval of semantic knowledge and representation of emotional information. Interestingly, when the task was switched to object recognition from face recognition, the functional connectivity between the inferior occipital gyrus and the rest of the face-selective regions were significantly reduced, suggesting that this region may serve as an entry node in the face-processing network. In sum, our study provides empirical evidence for cognitive and neural models of face recognition and helps elucidate the neural mechanisms underlying face recognition at the network level.

  17. AN APPLICATION OF HYBRID CLUSTERING AND NEURAL BASED PREDICTION MODELLING FOR DELINEATION OF MANAGEMENT ZONES

    Directory of Open Access Journals (Sweden)

    Babankumar S. Bansod

    2011-02-01

    Full Text Available Starting from descriptive data on crop yield and various other properties, the aim of this study is to reveal the trends on soil behaviour, such as crop yield. This study has been carried out by developing web application that uses a well known technique- Cluster Analysis. The cluster analysis revealed linkages between soil classes for the same field as well as between different fields, which can be partly assigned to crops rotation and determination of variable soil input rates. A hybrid clustering algorithm has been developed taking into account the traits of two clustering technologies: i Hierarchical clustering, ii K-means clustering. This hybrid clustering algorithm is applied to sensor- gathered data about soil and analysed, resulting in the formation of well delineatedmanagement zones based on various properties of soil, such as, ECa , crop yield, etc. One of the purposes of the study was to identify the main factors affecting the crop yield and the results obtained were validated with existing techniques. To accomplish this purpose, geo-referenced soil information has been examined. Also, based on this data, statistical method has been used to classify and characterize the soil behaviour. This is done using a prediction model, developed to predict the unknown behaviour of clusters based on the known behaviour of other clusters. In predictive modeling, data has been collected for the relevant predictors, a statistical model has been formulated, predictions were made and the model can be validated (or revised as additional data becomes available. The model used in the web application has been formed taking into account neural network based minimum hamming distance criterion.

  18. A HIERARCHICAL INTRUSION DETECTION ARCHITECTURE FOR WIRELESS SENSOR NETWORKS

    Directory of Open Access Journals (Sweden)

    Hossein Jadidoleslamy

    2011-10-01

    Full Text Available Networks protection against different types of attacks is one of most important posed issue into the network andinformation security application domains. This problem on Wireless Sensor Networks (WSNs, in attention to theirspecial properties, has more importance. Now, there are some of proposed architectures and guide lines to protectWireless Sensor Networks (WSNs against different types of intrusions; but any one of them do not has acomprehensive view to this problem and they are usually designed and implemented in single-purpose; but, theproposed design in this paper tries to has been a comprehensive view to this issue by presenting a complete andcomprehensive Intrusion Detection Architecture (IDA. The main contribution of this architecture is its hierarchicalstructure; i.e., it is designed and applicable, in one or two levels, consistent to the application domain and itsrequired security level. Focus of this paper is on the clustering WSNs, designing and deploying Cluster-basedIntrusion Detection System (CIDS on cluster-heads and Wireless Sensor Network wide level Intrusion DetectionSystem (WSNIDS on the central server. Suppositions of the WSN and Intrusion Detection Architecture (IDA are:static and heterogeneous network, hierarchical and clustering structure, clusters' overlapping and using hierarchicalrouting protocol such as LEACH, but along with minor changes. Finally, the proposed idea has been verified bydesigning a questionnaire, representing it to some (about 50 people experts and then, analyzing and evaluating itsacquired results.

  19. Quasar Evolution Driven by Galaxy Encounters in Hierarchical Structures

    CERN Document Server

    Menci,