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Sample records for hierarchical agglomerative clustering

  1. 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.

  2. PERFORMANCE OF SELECTED AGGLOMERATIVE HIERARCHICAL CLUSTERING METHODS

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    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.

  3. 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.

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

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    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.

  5. 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...

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

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    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.

  7. 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...

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

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    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

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

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    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

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

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    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.

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

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    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.

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

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    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

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

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    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

  14. 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.

  15. Codebook Generation Using Partition and Agglomerative Clustering

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    CHANG, C.-T.

    2011-08-01

    Full Text Available In this paper, we present a codebook generation algorithm to produce a codebook with lower distortion. Our method combines a fast codebook generation algorithm (CGAUCD with doubling technique and fast agglomerative clustering algorithm (FACA to generate a codebook with less computing time and lower distortion. Instead of using FACA directly to divide training vectors into M clusters, our proposed method first generates qM clusters from these training vectors, where q>1 is an integer, and then applies FACA to merge these qM clusters into M cells. This is due to the computational complexity of CGAUCD with doubling technique is less than that of FACA. These M cluster centers are used as the initial codebook for CGAUCD. Using three real images as the training set, our method can reduce the MSE and computing time of FPNN+CGAUCD, which is the available best method to our knowledge, by 0.19 to 0.38 and 74.6% to 84.3%, respectively.

  16. URL Mining Using Agglomerative Clustering Algorithm

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    Chinmay R. Deshmukh

    2015-02-01

    Full Text Available Abstract The tremendous growth of the web world incorporates application of data mining techniques to the web logs. Data Mining and World Wide Web encompasses an important and active area of research. Web log mining is analysis of web log files with web pages sequences. Web mining is broadly classified as web content mining web usage mining and web structure mining. Web usage mining is a technique to discover usage patterns from Web data in order to understand and better serve the needs of Web-based applications. URL mining refers to a subclass of Web mining that helps us to investigate the details of a Uniform Resource Locator. URL mining can be advantageous in the fields of security and protection. The paper introduces a technique for mining a collection of user transactions with an Internet search engine to discover clusters of similar queries and similar URLs. The information we exploit is a clickthrough data each record consist of a users query to a search engine along with the URL which the user selected from among the candidates offered by search engine. By viewing this dataset as a bipartite graph with the vertices on one side corresponding to queries and on the other side to URLs one can apply an agglomerative clustering algorithm to the graphs vertices to identify related queries and URLs.

  17. Fast agglomerative clustering using a k-nearest neighbor graph.

    Science.gov (United States)

    Fränti, Pasi; Virmajoki, Olli; Hautamäki, Ville

    2006-11-01

    We propose a fast agglomerative clustering method using an approximate nearest neighbor graph for reducing the number of distance calculations. The time complexity of the algorithm is improved from O(tauN2) to O(tauNlogN) at the cost of a slight increase in distortion; here, tau denotes the number of nearest neighbor updates required at each iteration. According to the experiments, a relatively small neighborhood size is sufficient to maintain the quality close to that of the full search.

  18. Agglomerative concentric hypersphere clustering applied to structural damage detection

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    Silva, Moisés; Santos, Adam; Santos, Reginaldo; Figueiredo, Eloi; Sales, Claudomiro; Costa, João C. W. A.

    2017-08-01

    The present paper proposes a novel cluster-based method, named as agglomerative concentric hypersphere (ACH), to detect structural damage in engineering structures. Continuous structural monitoring systems often require unsupervised approaches to automatically infer the health condition of a structure. However, when a structure is under linear and nonlinear effects caused by environmental and operational variability, data normalization procedures are also required to overcome these effects. The proposed approach aims, through a straightforward clustering procedure, to discover automatically the optimal number of clusters, representing the main state conditions of a structural system. Three initialization procedures are introduced to evaluate the impact of deterministic and stochastic initializations on the performance of this approach. The ACH is compared to state-of-the-art approaches, based on Gaussian mixture models and Mahalanobis squared distance, on standard data sets from a post-tensioned bridge located in Switzerland: the Z-24 Bridge. The proposed approach demonstrates more efficiency in modeling the normal condition of the structure and its corresponding main clusters. Furthermore, it reveals a better classification performance than the alternative ones in terms of false-positive and false-negative indications of damage, demonstrating a promising applicability in real-world structural health monitoring scenarios.

  19. 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...

  20. AGGLOMERATIVE CLUSTERING OF SOUND RECORD SPEECH SEGMENTS BASED ON BAYESIAN INFORMATION CRITERION

    Directory of Open Access Journals (Sweden)

    O. Yu. Kydashev

    2013-01-01

    Full Text Available This paper presents the detailed description of agglomerative clustering system implementation for speech segments based on Bayesian information criterion. Numerical experiment results with different acoustic features, as well as the full and diagonal covariance matrices application are given. The error rate DER equal to 6.4% for audio records of radio «Svoboda» was achieved by means of designed system.

  1. Application of agglomerative clustering for analyzing phylogenetically on bacterium of saliva

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    Bustamam, A.; Fitria, I.; Umam, K.

    2017-07-01

    Analyzing population of Streptococcus bacteria is important since these species can cause dental caries, periodontal, halitosis (bad breath) and more problems. This paper will discuss the phylogenetically relation between the bacterium Streptococcus in saliva using a phylogenetic tree of agglomerative clustering methods. Starting with the bacterium Streptococcus DNA sequence obtained from the GenBank, then performed characteristic extraction of DNA sequences. The characteristic extraction result is matrix form, then performed normalization using min-max normalization and calculate genetic distance using Manhattan distance. Agglomerative clustering technique consisting of single linkage, complete linkage and average linkage. In this agglomerative algorithm number of group is started with the number of individual species. The most similar species is grouped until the similarity decreases and then formed a single group. Results of grouping is a phylogenetic tree and branches that join an established level of distance, that the smaller the distance the more the similarity of the larger species implementation is using R, an open source program.

  2. Neutrosophic Hierarchical Clustering Algoritms

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    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.

  3. Hierarchical clustering techniques for image database organization and summarization

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    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.

  4. 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

  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. Performance Analysis of Hierarchical Clustering Algorithm

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    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.

  8. A Novel Divisive Hierarchical Clustering Algorithm for Geospatial Analysis

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    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.

  9. 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.

  10. 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.

  11. 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.

  12. Improving CLOPE’s Profit Value and Stability with an Optimized Agglomerative Approach

    Directory of Open Access Journals (Sweden)

    Yefeng Li

    2015-06-01

    Full Text Available CLOPE (Clustering with sLOPE is a simple and fast histogram-based clustering algorithm for categorical data. However, given the same data set with the same input parameter, the clustering results by this algorithm would possibly be different if the transactions are input in a different sequence. In this paper, a hierarchical clustering framework is proposed as an extension of CLOPE to generate stable and satisfactory clustering results based on an optimized agglomerative merge process. The new clustering profit is defined as the merge criteria and the cluster graph structure is proposed to optimize the merge iteration process. The experiments conducted on two datasets both demonstrate that the agglomerative approach achieves stable clustering results with a better profit value, but costs much more time due to the worse complexity.

  13. 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.

  14. Research in clustering algorithm based on local agglomerative characteristics%利用局部集聚特性的聚类算法的研究

    Institute of Scientific and Technical Information of China (English)

    牛习现; 赵立川

    2011-01-01

    基于SNN相似性和密度的聚类算法是当前主要的无监督聚类方法之一,该类算法在发现不同大小形状簇的聚类过程中都取得了较好的结果.但是该类算法也存在局限性,如Jarvis-Patrick算法通过单连结的方式发现簇,可能分割真正的簇或者合并应该保持分离的簇,而SNN密度类算法的Eps,MinPts参数的确定对用户来说是比较困难的.针对该类问题,本文对聚类过程中的局部集聚特征进行了分析和定义,提出了利用数据的局部集聚特征来控制聚类过程的的聚类算法.通过验证,该算法对发现不同密度以及任意形状的数据集合的聚类分析问题是有效的,突出了数据分析的局部集聚特征,改进了数据聚类的质量.%The SNN similarity and density based clustering, as one of the most important unsupervised clustering method, has been proved to produce good results in finding clusters of various sizes and shapes. But these algorithms still have some limitations. For example, Jarvis-Patrick scheme of finding clusters by single link, may separate real clusters or merge clusters which should be kept separated in certain situations, and the determination of Eps and MinPts, the parameters of SNN density method, is hard for users. To deal with these problems, the paper gives analysis and definition of local agglomerative characteristics presented in clustering procedure; then proposes a new clustering algorithm which use local gathering features to control clustering progress. The algorithm can work well in finding different size and density clusters, highlighting the local features of data analysis and improving the quality of data clusters.

  15. 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.

  16. 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.

  17. 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…

  18. 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…

  19. 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

  20. 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...

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

    Directory of Open Access Journals (Sweden)

    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.

  2. 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.

  3. 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...

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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...

  9. 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...

  10. 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.

  11. 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.

  12. 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…

  13. 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.

  14. 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...

  15. 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).

  16. 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 ...

  17. 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...

  18. 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...

  19. 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.

  20. 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

  1. 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.

  2. 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...

  3. 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.

  4. 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.

  5. 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...

  6. 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.

  7. 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.

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. Evolution of Terrorist Network using Clustered approach: A Case study

    DEFF Research Database (Denmark)

    2011-01-01

    In the paper we present a cluster based approach for terrorist network evolution. We have applied hierarchical agglomerative clustering approach to 9/11 case study. We show that, how individual actors who are initially isolated from each other are converted in small clusters and result in a fully...... evolved network. This method of network evolution can help intelligence security analysts to understand the structure of the network....

  16. 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.

  17. 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.

  18. 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.

  19. Hybrid secure beamforming and vehicle selection using hierarchical agglomerative clustering for C-RAN-based vehicle-to-infrastructure communications in vehicular cyber-physical systems

    National Research Council Canada - National Science Library

    Xu, Dongyang; Ren, Pinyi; Du, Qinghe; Sun, Li

    2016-01-01

    ...–enhancing mechanisms in the physical layer. In this article, we propose a hybrid beamforming and vehicle-selection framework for vehicle-to-infrastructure communications to broadcast high-speed confidential messages...

  20. 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.

  1. 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 ...

  2. 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...

  3. 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.

  4. 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.

  5. 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.

  6. 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

  7. 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

  8. 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.

  9. 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

  10. 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.

  11. 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.

  12. 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

  13. 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...

  14. 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.

  15. 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

  16. 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.

  17. A weight's agglomerative method for detecting communities in weighted networks based on weight's similarity

    Institute of Scientific and Technical Information of China (English)

    Shen Yi

    2011-01-01

    This paper proposes the new definition of the community structure of the weighted networks that groups of nodes in which the edge's weights distribute uniformly but at random between them. It can describe the steady connections between nodes or some similarity between nodes' functions effectively.In order to detect the community structure efficiently,a threshold coefficient K to evaluate the equivalence of edges' weights and a new weighted modularity based on the weight's similarity are proposed. Then, constructing the weighted matrix and using the agglomerative mechanism,it presents a weight's agglomerative method based on optimizing the modularity to detect communities. For a network with n nodes, the algorithm can detect the community structure in time 0(n2 logn2).Simulations on networks show that the algorithm has higher accuracy and precision than the existing techniques. Furthermore, with the change of K the algorithm discovers a special hierarchical organization which can describe the various steady connections between nodes in groups.

  18. 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.

  19. 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.

  20. 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...

  1. 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.

  2. 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.

  3. Agglomerative percolation on the Bethe lattice and the triangular cactus

    Science.gov (United States)

    Chae, Huiseung; Yook, Soon-Hyung; Kim, Yup

    2013-08-01

    Agglomerative percolation (AP) on the Bethe lattice and the triangular cactus is studied to establish the exact mean-field theory for AP. Using the self-consistent simulation method based on the exact self-consistent equations, the order parameter P∞ and the average cluster size S are measured. From the measured P∞ and S, the critical exponents βk and γk for k = 2 and 3 are evaluated. Here, βk and γk are the critical exponents for P∞ and S when the growth of clusters spontaneously breaks the Zk symmetry of the k-partite graph. The obtained values are β2 = 1.79(3), γ2 = 0.88(1), β3 = 1.35(5) and γ3 = 0.94(2). By comparing these exponents with those for ordinary percolation (β∞ = 1 and γ∞ = 1), we also find β∞ γ3 > γ2. These results quantitatively verify the conjecture that the AP model belongs to a new universality class if the Zk symmetry is broken spontaneously, and the new universality class depends on k.

  4. 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.

  5. 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

  6. 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.

  7. 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.

  8. 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.

  9. 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...

  10. 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.

  11. 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...

  12. Modules Identification in Gene Positive Networks of Hepatocellular Carcinoma Using Pearson Agglomerative Method and Pearson Cohesion Coupling Modularity

    OpenAIRE

    Hu, Jinyu; Gao, Zhiwei

    2012-01-01

    In this study, a gene positive network is proposed based on a weighted undirected graph, where the weight represents the positive correlation of the genes. A Pearson agglomerative clustering algorithm is employed to build a clustering tree, where dotted lines cut the tree from bottom to top leading to a number of subsets of the modules. In order to achieve better module partitions, the Pearson correlation coefficient modularity is addressed to seek optimal module decomposition by selecting an...

  13. 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...

  14. 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...

  15. On Comparison of Clustering Methods for Pharmacoepidemiological Data.

    Science.gov (United States)

    Feuillet, Fanny; Bellanger, Lise; Hardouin, Jean-Benoit; Victorri-Vigneau, Caroline; Sébille, Véronique

    2015-01-01

    The high consumption of psychotropic drugs is a public health problem. Rigorous statistical methods are needed to identify consumption characteristics in post-marketing phase. Agglomerative hierarchical clustering (AHC) and latent class analysis (LCA) can both provide clusters of subjects with similar characteristics. The objective of this study was to compare these two methods in pharmacoepidemiology, on several criteria: number of clusters, concordance, interpretation, and stability over time. From a dataset on bromazepam consumption, the two methods present a good concordance. AHC is a very stable method and it provides homogeneous classes. LCA is an inferential approach and seems to allow identifying more accurately extreme deviant behavior.

  16. 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.

  17. 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.

  18. 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.

  19. 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

  20. 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...

  1. 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.

  2. 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/

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

    Directory of Open Access Journals (Sweden)

    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

  4. 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.

  5. 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.

  6. 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.

  7. 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.

  8. 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.

  9. 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…

  10. [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

  11. 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

  12. 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.

  13. 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.

  14. 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

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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.

  3. 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

    -means, hierarchical clustering and the incorporation of the SOM analysis into criteria based approaches to assess pest risk.

  4. 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.

  5. 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.

  6. 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).

  7. 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.

  8. 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.

  9. 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.

  10. 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...

  11. 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

  12. 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.

  13. 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 ...

  14. 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

  15. 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

  16. 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

  17. 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.

  18. Fast redshift clustering with the Baire (ultra) metric

    CERN Document Server

    Murtagh, Fionn

    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. 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 cos\\ tly 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 develop a clusterwise nearest neighbor regression procedure for this.

  19. 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...

  20. 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.

  1. 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...

  2. 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 ...

  3. 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.

  4. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  6. CNEM: Cluster Based Network Evolution Model

    Directory of Open Access Journals (Sweden)

    Sarwat Nizamani

    2015-01-01

    Full Text Available This paper presents a network evolution model, which is based on the clustering approach. The proposed approach depicts the network evolution, which demonstrates the network formation from individual nodes to fully evolved network. An agglomerative hierarchical clustering method is applied for the evolution of network. In the paper, we present three case studies which show the evolution of the networks from the scratch. These case studies include: terrorist network of 9/11 incidents, terrorist network of WMD (Weapons Mass Destruction plot against France and a network of tweets discussing a topic. The network of 9/11 is also used for evaluation, using other social network analysis methods which show that the clusters created using the proposed model of network evolution are of good quality, thus the proposed method can be used by law enforcement agencies in order to further investigate the criminal networks

  7. 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.

  8. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  10. 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

  11. 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.

  12. 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.

  13. 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...

  14. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

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

    Directory of Open Access Journals (Sweden)

    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.

  18. 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

  19. 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.

  20. 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.

  1. 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.

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

    Directory of Open Access Journals (Sweden)

    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.

  3. 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

  4. Data Clustering

    Science.gov (United States)

    Wagstaff, Kiri L.

    2012-03-01

    particular application involves considerations of the kind of data being analyzed, algorithm runtime efficiency, and how much prior knowledge is available about the problem domain, which can dictate the nature of clusters sought. Fundamentally, the clustering method and its representations of clusters carries with it a definition of what a cluster is, and it is important that this be aligned with the analysis goals for the problem at hand. In this chapter, I emphasize this point by identifying for each algorithm the cluster representation as a model, m_j , even for algorithms that are not typically thought of as creating a “model.” This chapter surveys a basic collection of clustering methods useful to any practitioner who is interested in applying clustering to a new data set. The algorithms include k-means (Section 25.2), EM (Section 25.3), agglomerative (Section 25.4), and spectral (Section 25.5) clustering, with side mentions of variants such as kernel k-means and divisive clustering. The chapter also discusses each algorithm’s strengths and limitations and provides pointers to additional in-depth reading for each subject. Section 25.6 discusses methods for incorporating domain knowledge into the clustering process. This chapter concludes with a brief survey of interesting applications of clustering methods to astronomy data (Section 25.7). The chapter begins with k-means because it is both generally accessible and so widely used that understanding it can be considered a necessary prerequisite for further work in the field. EM can be viewed as a more sophisticated version of k-means that uses a generative model for each cluster and probabilistic item assignments. Agglomerative clustering is the most basic form of hierarchical clustering and provides a basis for further exploration of algorithms in that vein. Spectral clustering permits a departure from feature-vector-based clustering and can operate on data sets instead represented as affinity, or similarity

  5. 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.

  6. 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...

  7. 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.

  8. 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.

  9. [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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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...

  16. 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

  17. Modules Identification in Gene Positive Networks of Hepatocellular Carcinoma Using Pearson Agglomerative Method and Pearson Cohesion Coupling Modularity

    Directory of Open Access Journals (Sweden)

    Jinyu Hu

    2012-01-01

    Full Text Available In this study, a gene positive network is proposed based on a weighted undirected graph, where the weight represents the positive correlation of the genes. A Pearson agglomerative clustering algorithm is employed to build a clustering tree, where dotted lines cut the tree from bottom to top leading to a number of subsets of the modules. In order to achieve better module partitions, the Pearson correlation coefficient modularity is addressed to seek optimal module decomposition by selecting an optimal threshold value. For the liver cancer gene network under study, we obtain a strong threshold value at 0.67302, and a very strong correlation threshold at 0.80086. On the basis of these threshold values, fourteen strong modules and thirteen very strong modules are obtained respectively. A certain degree of correspondence between the two types of modules is addressed as well. Finally, the biological significance of the two types of modules is analyzed and explained, which shows that these modules are closely related to the proliferation and metastasis of liver cancer. This discovery of the new modules may provide new clues and ideas for liver cancer treatment.

  18. 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.

  19. 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.

  20. 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

  1. Graph coarsening and clustering on the GPU

    NARCIS (Netherlands)

    Fagginger Auer, B.O.; Bisseling, R.H.

    2013-01-01

    Agglomerative clustering is an effective greedy way to quickly generate graph clusterings of high modularity in a small amount of time. In an effort to use the power offered by multi-core CPU and GPU hardware to solve the clustering problem, we introduce a fine-grained sharedmemory parallel graph co

  2. Graph coarsening and clustering on the GPU

    NARCIS (Netherlands)

    Fagginger Auer, B.O.; Bisseling, R.H.

    2013-01-01

    Agglomerative clustering is an effective greedy way to quickly generate graph clusterings of high modularity in a small amount of time. In an effort to use the power offered by multi-core CPU and GPU hardware to solve the clustering problem, we introduce a fine-grained sharedmemory parallel graph co

  3. Graph coarsening and clustering on the GPU

    NARCIS (Netherlands)

    Fagginger Auer, B.O.|info:eu-repo/dai/nl/326659072; Bisseling, R.H.|info:eu-repo/dai/nl/304828068

    2013-01-01

    Agglomerative clustering is an effective greedy way to quickly generate graph clusterings of high modularity in a small amount of time. In an effort to use the power offered by multi-core CPU and GPU hardware to solve the clustering problem, we introduce a fine-grained sharedmemory parallel graph

  4. 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.

  5. 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...

  6. 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...

  7. Practical Introduction to Clustering Data

    CERN Document Server

    Hartmann, Alexander K

    2016-01-01

    Data clustering is an approach to seek for structure in sets of complex data, i.e., sets of "objects". The main objective is to identify groups of objects which are similar to each other, e.g., for classification. Here, an introduction to clustering is given and three basic approaches are introduced: the k-means algorithm, neighbour-based clustering, and an agglomerative clustering method. For all cases, C source code examples are given, allowing for an easy implementation.

  8. 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

  9. 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.

  10. 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

  11. Cluster analysis of WIBS single particle bioaerosol data

    Directory of Open Access Journals (Sweden)

    N. H. Robinson

    2012-09-01

    Full Text Available Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial datasets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Waveband Integrated Bioaerosol Sensor (WIBS. The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL before being applied to two separate contemporaneous ambient WIBS datasets recorded in a forest site in Colorado, USA as part of the BEACHON-RoMBAS project. Cluster analysis results between both datasets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity to represent: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long term online PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics is improved.

  12. Cluster analysis of WIBS single particle bioaerosol data

    Science.gov (United States)

    Robinson, N. H.; Allan, J. D.; Huffman, J. A.; Kaye, P. H.; Foot, V. E.; Gallagher, M.

    2012-09-01

    Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial datasets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Waveband Integrated Bioaerosol Sensor (WIBS). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS datasets recorded in a forest site in Colorado, USA as part of the BEACHON-RoMBAS project. Cluster analysis results between both datasets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long term online PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics is improved.

  13. 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

  14. AN EFFICIENT APPROACH TO IMPROVE ARABIC DOCUMENTS CLUSTERING BASED ON A NEW KEYPHRASES EXTRACTION ALGORITHM

    Directory of Open Access Journals (Sweden)

    Hanane FROUD

    2013-11-01

    Full Text Available Document Clustering algorithms goal is to create clusters that are coherent internally, but clearly different from each other. The useful expressions in the documents is often accompanied by a large amount of noise that is caused by the use of unnecessary words, so it is indispensable to eliminate it and keeping just the useful information. Keyphrases extraction systems in Arabic are new phenomena. A number of Text Mining applications can use it to improve her results. The Keyphrases are defined as phrases that capture the main topics discussed in document; they offer a brief and precise summary of document content. Therefore, it can be a good solution to get rid of the existent noise from documents. In this paper, we propose a new method to solve the problem cited above especially for Arabic language documents, which is one of the most complex languages, by using a new Keyphrases extraction algorithm based on the Suffix Tree data structure (KpST. To evaluate our approach, we conduct an experimental study on Arabic Documents Clustering using the most popular approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with seven linkage techniques and a variety of distance functions and similarity measures to perform Arabic Document Clustering task. The obtained results show that our approach for extracting Keyphrases improves the clustering results.

  15. R-clustering for egocentric video segmentation

    NARCIS (Netherlands)

    Talavera Martínez, Estefanía

    2015-01-01

    In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering the

  16. R-clustering for egocentric video segmentation

    NARCIS (Netherlands)

    Talavera Martínez, Estefanía; Radeva, Petia

    2015-01-01

    In this paper, we present a new method for egocentric video temporal segmentation based on integrating a statistical mean change detector and agglomerative clustering(AC) within an energy-minimization framework. Given the tendency of most AC methods to oversegment video sequences when clustering

  17. 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.

  18. Clustering of hydrological data: a review of methods for runoff predictions in ungauged basins

    Science.gov (United States)

    Dogulu, Nilay; Kentel, Elcin

    2017-04-01

    There is a great body of research that has looked into the challenge of hydrological predictions in ungauged basins as driven by the Prediction in Ungauged Basins (PUB) initiative of the International Association of Hydrological Sciences (IAHS). Transfer of hydrological information (e.g. model parameters, flow signatures) from gauged to ungauged catchment, often referred as "regionalization", is the main objective and benefits from identification of hydrologically homogenous regions. Within this context, indirect representation of hydrologic similarity for ungauged catchments, which is not a straightforward task due to absence of streamflow measurements and insufficient knowledge of hydrologic behavior, has been explored in the literature. To this aim, clustering methods have been widely adopted. While most of the studies employ hard clustering techniques such as hierarchical (divisive or agglomerative) clustering, there have been more recent attempts taking advantage of fuzzy set theory (fuzzy clustering) and nonlinear methods (e.g. self-organizing maps). The relevant research findings from this fundamental task of hydrologic sciences have revealed the value of different clustering methods for improved understanding of catchment hydrology. However, despite advancements there still remains challenges and yet opportunities for research on clustering for regionalization purposes. The present work provides an overview of clustering techniques and their applications in hydrology with focus on regionalization for the PUB problem. Identifying their advantages and disadvantages, we discuss the potential of innovative clustering methods and reflect on future challenges in view of the research objectives of the PUB initiative.

  19. 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

  20. Clustering of the Self-Organizing Map based Approach in Induction Machine Rotor Faults Diagnostics

    Directory of Open Access Journals (Sweden)

    Ahmed TOUMI

    2009-12-01

    Full Text Available Self-Organizing Maps (SOM is an excellent method of analyzingmultidimensional data. The SOM based classification is attractive, due to itsunsupervised learning and topology preserving properties. In this paper, theperformance of the self-organizing methods is investigated in induction motorrotor fault detection and severity evaluation. The SOM is based on motor currentsignature analysis (MCSA. The agglomerative hierarchical algorithms using theWard’s method is applied to automatically dividing the map into interestinginterpretable groups of map units that correspond to clusters in the input data. Theresults obtained with this approach make it possible to detect a rotor bar fault justdirectly from the visualization results. The system is also able to estimate theextent of rotor faults.

  1. Nonlinear multidimensional scaling and visualization of earthquake clusters over space, time and feature space

    Directory of Open Access Journals (Sweden)

    W. Dzwinel

    2005-01-01

    Full Text Available We present a novel technique based on a multi-resolutional clustering and nonlinear multi-dimensional scaling of earthquake patterns to investigate observed and synthetic seismic catalogs. The observed data represent seismic activities around the Japanese islands during 1997-2003. The synthetic data were generated by numerical simulations for various cases of a heterogeneous fault governed by 3-D elastic dislocation and power-law creep. At the highest resolution, we analyze the local cluster structures in the data space of seismic events for the two types of catalogs by using an agglomerative clustering algorithm. We demonstrate that small magnitude events produce local spatio-temporal patches delineating neighboring large events. Seismic events, quantized in space and time, generate the multi-dimensional feature space characterized by the earthquake parameters. Using a non-hierarchical clustering algorithm and nonlinear multi-dimensional scaling, we explore the multitudinous earthquakes by real-time 3-D visualization and inspection of the multivariate clusters. At the spatial resolutions characteristic of the earthquake parameters, all of the ongoing seismicity both before and after the largest events accumulates to a global structure consisting of a few separate clusters in the feature space. We show that by combining the results of clustering in both low and high resolution spaces, we can recognize precursory events more precisely and unravel vital information that cannot be discerned at a single resolution.

  2. A clustering approach applied to time-lapse ERT interpretation - Case study of Lascaux cave

    Science.gov (United States)

    Xu, Shan; Sirieix, Colette; Riss, Joëlle; Malaurent, Philippe

    2017-09-01

    The Lascaux cave, located in southwest France, is one of the most important prehistoric cave in the world that shows Paleolithic paintings. This study aims to characterize the structure of the weathered epikarst setting located above the cave using Time-Lapse Electrical Resistivity Tomography (ERT) combined with local hydrogeological and climatic environmental data. Twenty ERT profiles were carried out for two years and helped us to record the seasonal and spatial variations of the electrical resistivity of the hydraulic upstream area of the Lascaux cave. The 20 interpreted resistivity models were merged into a single synthetic model using a multidimensional statistical method (Hierarchical Agglomerative Clustering). The individual blocks from the synthetic model associated with a similar resistivity variability were gathered into 7 clusters. We combined the resistivity temporal variations with climatic and hydrogeological data to propose a geo-electrical model that relates to a conceptual geological model. We provide a geological interpretation for each cluster regarding epikarst features. The superficial clusters (no 1 & 2) are linked to effective rainfall and trees, probably a fractured limestone. Another two clusters (no 6 & 7) are linked to detrital formations (sand and clay respectively). The cluster 3 may correspond to a marly limestone that forms a non-permeable horizon. Finally, the electrical behavior of the last two clusters (no 4 & 5) is correlated with the variation of flow rate; they may be a privileged feed zone of the flow in the cave.

  3. 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.

  4. 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...

  5. 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.

  6. 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.

  7. 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.

  8. Using cluster analysis of cytokines to identify patterns of inflammation in hospitalized patients with community-acquired pneumonia: a pilot study

    Science.gov (United States)

    Wiemken, Timothy L; Kelley, Robert R; Fernandez-Botran, Rafael; Mattingly, William A.; Arnold, Forest W.; Furmanek, Stephen P; Restrepo, Marcos I; Chalmers, James D; Peyrani, Paula; Cavallazzi, Rodrigo; Bordon, Jose; Aliberti, Stefano; Ramirez, Julio A.

    2017-01-01

    Introduction Patients with severe community-acquired pneumonia (CAP) are believed to have an exaggerated inflammatory response to bacterial infection. Therapies aiming to modulate the inflammatory response have been largely unsuccessful, perhaps reflecting that CAP is a heterogeneous disorder that cannot be modulated by a single anti-inflammatory approach. We hypothesize that the host inflammatory response to pneumonia may be characterized by distinct cytokine patterns, which can be harnessed for personalized therapies. Methods Here, we use hierarchical cluster analysis of cytokines to examine if patterns of inflammatory response in 13 hospitalized patients with CAP can be defined. This was a secondary data analysis of the Community-Acquired Pneumonia Inflammatory Study Group (CAPISG) database. The following cytokines were measured in plasma and sputum on the day of admission: interleukin (IL)-1β, IL-1 receptor antagonist (IL-1ra), IL-6, CXCL8 (IL-8), IL-10, IL-12p40, IL-17, interferon (IFN)γ, tumor necrosis factor (TNF)α, and CXCL10 (IP-10). Hierarchical agglomerative clustering algorithms were used to evaluate clusters of patients within plasma and sputum cytokine determinations. Results A total of thirteen patients were included in this pilot study. Cluster analysis identified distinct inflammatory response patterns of cytokines in the plasma, sputum, and the ratio of plasma to sputum. Conclusions Inflammatory response patterns in plasma and sputum can be identified in hospitalized patients with CAP. Characterization of the local and systemic inflammatory response may help to better discriminate patients for enrollment into clinical trials of immunomodulatory therapies.

  9. 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.

  10. 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.

  11. 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)

  12. 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.

  13. ARC: Automated Resource Classifier for agglomerative functional classification of prokaryotic proteins using annotation texts

    Indian Academy of Sciences (India)

    Muthiah Gnanamani; Naveen Kumar; Srinivasan Ramachandran

    2007-08-01

    Functional classification of proteins is central to comparative genomics. The need for algorithms tuned to enable integrative interpretation of analytical data is felt globally. The availability of a general, automated software with built-in flexibility will significantly aid this activity. We have prepared ARC (Automated Resource Classifier), which is an open source software meeting the user requirements of flexibility. The default classification scheme based on keyword match is agglomerative and directs entries into any of the 7 basic non-overlapping functional classes: Cell wall, Cell membrane and Transporters ($\\mathcal{C}$), Cell division ($\\mathcal{D}$), Information ($\\mathcal{I}$), Translocation ($\\mathcal{L}$), Metabolism ($\\mathcal{M}$), Stress($\\mathcal{R}$), Signal and communication($\\mathcal{S}$) and 2 ancillary classes: Others ($\\mathcal{O}$) and Hypothetical ($\\mathcal{H}$). The keyword library of ARC was built serially by first drawing keywords from Bacillus subtilis and Escherichia coli K12. In subsequent steps, this library was further enriched by collecting terms from archaeal representative Archaeoglobus fulgidus, Gene Ontology, and Gene Symbols. ARC is 94.04% successful on 6,75,663 annotated proteins from 348 prokaryotes. Three examples are provided to illuminate the current perspectives on mycobacterial physiology and costs of proteins in 333 prokaryotes. ARC is available at http://arc.igib.res.in.

  14. 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(...

  15. 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.

  16. Methods for simultaneously identifying coherent local clusters with smooth global patterns in gene expression profiles

    Directory of Open Access Journals (Sweden)

    Lee Yun-Shien

    2008-03-01

    Full Text Available Abstract Background The hierarchical clustering tree (HCT with a dendrogram 1 and the singular value decomposition (SVD with a dimension-reduced representative map 2 are popular methods for two-way sorting the gene-by-array matrix map employed in gene expression profiling. While HCT dendrograms tend to optimize local coherent clustering patterns, SVD leading eigenvectors usually identify better global grouping and transitional structures. Results This study proposes a flipping mechanism for a conventional agglomerative HCT using a rank-two ellipse (R2E, an improved SVD algorithm for sorting purpose seriation by Chen 3 as an external reference. While HCTs always produce permutations with good local behaviour, the rank-two ellipse seriation gives the best global grouping patterns and smooth transitional trends. The resulting algorithm automatically integrates the desirable properties of each method so that users have access to a clustering and visualization environment for gene expression profiles that preserves coherent local clusters and identifies global grouping trends. Conclusion We demonstrate, through four examples, that the proposed method not only possesses better numerical and statistical properties, it also provides more meaningful biomedical insights than other sorting algorithms. We suggest that sorted proximity matrices for genes and arrays, in addition to the gene-by-array expression matrix, can greatly aid in the search for comprehensive understanding of gene expression structures. Software for the proposed methods can be obtained at http://gap.stat.sinica.edu.tw/Software/GAP.

  17. Compulsive buying disorder clustering based on sex, age, onset and personality traits.

    Science.gov (United States)

    Granero, Roser; Fernández-Aranda, Fernando; Baño, Marta; Steward, Trevor; Mestre-Bach, Gemma; Del Pino-Gutiérrez, Amparo; Moragas, Laura; Mallorquí-Bagué, Núria; Aymamí, Neus; Goméz-Peña, Mónica; Tárrega, Salomé; Menchón, José M; Jiménez-Murcia, Susana

    2016-07-01

    In spite of the revived interest in compulsive buying disorder (CBD), its classification into the contemporary nosologic systems continues to be debated, and scarce studies have addressed heterogeneity in the clinical phenotype through methodologies based on a person-centered approach. To identify empirical clusters of CBD employing personality traits, as well as patients' sex, age and the age of CBD onset as indicators. An agglomerative hierarchical clustering method defining a combination of the Schwarz Bayesian Information Criterion and log-likelihood was used. Three clusters were identified in a sample of n=110 patients attending a specialized CBD unit a) "male compulsive buyers" reported the highest prevalence of comorbid gambling disorder and the lowest levels of reward dependence; b) "female low-dysfunctional" mainly included employed women, with the highest level of education, the oldest age of onset, the lowest scores in harm avoidance and the highest levels of persistence, self-directedness and cooperativeness; and c) "female highly-dysfunctional" with the youngest age of onset, the highest levels of comorbid psychopathology and harm avoidance, and the lowest score in self-directedness. Sociodemographic characteristics and personality traits can be used to determine CBD clusters which represent different clinical subtypes. These subtypes should be considered when developing assessment instruments, preventive programs and treatment interventions. Copyright © 2016 Elsevier Inc. All rights reserved.

  18. 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...

  19. 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

  20. Cluster analysis of WIBS single-particle bioaerosol data

    Science.gov (United States)

    Robinson, N. H.; Allan, J. D.; Huffman, J. A.; Kaye, P. H.; Foot, V. E.; Gallagher, M.

    2013-02-01

    Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs). The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL) before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity) to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP) measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

  1. Cluster analysis of WIBS single-particle bioaerosol data

    Directory of Open Access Journals (Sweden)

    N. H. Robinson

    2013-02-01

    Full Text Available Hierarchical agglomerative cluster analysis was performed on single-particle multi-spatial data sets comprising optical diameter, asymmetry and three different fluorescence measurements, gathered using two dual Wideband Integrated Bioaerosol Sensors (WIBSs. The technique is demonstrated on measurements of various fluorescent and non-fluorescent polystyrene latex spheres (PSL before being applied to two separate contemporaneous ambient WIBS data sets recorded in a forest site in Colorado, USA, as part of the BEACHON-RoMBAS project. Cluster analysis results between both data sets are consistent. Clusters are tentatively interpreted by comparison of concentration time series and cluster average measurement values to the published literature (of which there is a paucity to represent the following: non-fluorescent accumulation mode aerosol; bacterial agglomerates; and fungal spores. To our knowledge, this is the first time cluster analysis has been applied to long-term online primary biological aerosol particle (PBAP measurements. The novel application of this clustering technique provides a means for routinely reducing WIBS data to discrete concentration time series which are more easily interpretable, without the need for any a priori assumptions concerning the expected aerosol types. It can reduce the level of subjectivity compared to the more standard analysis approaches, which are typically performed by simple inspection of various ensemble data products. It also has the advantage of potentially resolving less populous or subtly different particle types. This technique is likely to become more robust in the future as fluorescence-based aerosol instrumentation measurement precision, dynamic range and the number of available metrics are improved.

  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. Suicide in the oldest old: an observational study and cluster analysis.

    Science.gov (United States)

    Sinyor, Mark; Tan, Lynnette Pei Lin; Schaffer, Ayal; Gallagher, Damien; Shulman, Kenneth

    2016-01-01

    The older population are at a high risk for suicide. This study sought to learn more about the characteristics of suicide in the oldest-old and to use a cluster analysis to determine if oldest-old suicide victims assort into clinically meaningful subgroups. Data were collected from a coroner's chart review of suicide victims in Toronto from 1998 to 2011. We compared two age groups (65-79 year olds, n = 335, and 80+ year olds, n = 191) and then conducted a hierarchical agglomerative cluster analysis using Ward's method to identify distinct clusters in the 80+ group. The younger and older age groups differed according to marital status, living circumstances and pattern of stressors. The cluster analysis identified three distinct clusters in the 80+ group. Cluster 1 was the largest (n = 124) and included people who were either married or widowed who had significantly more depression and somewhat more medical health stressors. In contrast, cluster 2 (n = 50) comprised people who were almost all single and living alone with significantly less identified depression and slightly fewer medical health stressors. All members of cluster 3 (n = 17) lived in a retirement residence or nursing home, and this group had the highest rates of depression, dementia, other mental illness and past suicide attempts. This is the first study to use the cluster analysis technique to identify meaningful subgroups among suicide victims in the oldest-old. The results reveal different patterns of suicide in the older population that may be relevant for clinical care. Copyright © 2015 John Wiley & Sons, Ltd.

  4. 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.

  5. 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.

  6. 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.

  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. 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...

  12. A New Keyphrases Extraction Method Based on Suffix Tree Data Structure for Arabic Documents Clustering

    Directory of Open Access Journals (Sweden)

    Issam SAHMOUDI

    2013-12-01

    Full Text Available Document Clustering is a branch of a larger area of scientific study kn own as data mining .which is an unsupervised classification using to find a structu re in a collection of unlabeled data. The useful information in the documents can be accompanied b y a large amount of noise words when using Full Tex t Representation, and therefore will affect negativel y the result of the clustering process. So it is w ith great need to eliminate the noise words and keeping just the useful information in order to enhance the qual ity of the clustering results. This problem occurs with di fferent degree for any language such as English, European, Hindi, Chinese, and Arabic Language. To o vercome this problem, in this paper, we propose a new and efficient Keyphrases extraction method base d on the Suffix Tree data structure (KpST, the extracted Keyphrases are then used in the clusterin g process instead of Full Text Representation. The proposed method for Keyphrases extraction is langua ge independent and therefore it may be applied to a ny language. In this investigation, we are interested to deal with the Arabic language which is one of th e most complex languages. To evaluate our method, we condu ct an experimental study on Arabic Documents using the most popular Clustering approach of Hiera rchical algorithms: Agglomerative Hierarchical algorithm with seven linkage techniques and a varie ty of distance functions and similarity measures to perform Arabic Document Clustering task. The obtain ed results show that our method for extracting Keyphrases increases the quality of the clustering results. We propose also to study the effect of using the stemming for the testing dataset to cluster it with the same documents clustering techniques and similarity/distance measures.

  13. 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...

  14. 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

  15. 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.

  16. 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.

  17. The outbreak of SARS mirrored by bibliometric mapping: Combining bibliographic coupling with the complete link cluster method

    Directory of Open Access Journals (Sweden)

    Bo Jarneving

    2007-01-01

    Full Text Available In this study a novel method of science mapping is presented which combines bibliographic coupling, as a measure of document-document similarity, with an agglomerative hierarchical cluster method. The focus in this study is on the mapping of so called ‘core documents’, a concept presented first in 1995 by Glänzel and Czerwon. The term ‘core document’ denote documents that have a central position in the research front in terms of many and strong bibliographic coupling links. The identification and mapping of core documents usually requires a large multidisciplinary research setting and in this study the 2003 volume of the Science Citation Index was applied. From this database, a sub-set of core documents reporting on the outbreak of SARS in 2002 was chosen for the demonstration of the application of this mapping method. It was demonstrated that the method, in this case, successfully identified interpretable research themes and that iterative clustering on two subsequent levels of cluster agglomeration may provide with useful and current information.

  18. A 350 ka record of climate change from Lake El'gygytgyn, Far East Russian Arctic: refining the pattern of climate modes by means of cluster analysis

    Directory of Open Access Journals (Sweden)

    U. Frank

    2013-07-01

    Full Text Available Rock magnetic, biochemical and inorganic records of the sediment cores PG1351 and Lz1024 from Lake El'gygytgyn, Chukotka peninsula, Far East Russian Arctic, were subject to a hierarchical agglomerative cluster analysis in order to refine and extend the pattern of climate modes as defined by Melles et al. (2007. Cluster analysis of the data obtained from both cores yielded similar results, differentiating clearly between the four climate modes warm, peak warm, cold and dry, and cold and moist. In addition, two transitional phases were identified, representing the early stages of a cold phase and slightly colder conditions during a warm phase. The statistical approach can thus be used to resolve gradual changes in the sedimentary units as an indicator of available oxygen in the hypolimnion in greater detail. Based upon cluster analyses on core Lz1024, the published succession of climate modes in core PG1351, covering the last 250 ka, was modified and extended back to 350 ka. Comparison to the marine oxygen isotope (δ18O stack LR04 (Lisiecki and Raymo, 2005 and the summer insolation at 67.5° N, with the extended Lake El'gygytgyn parameter records of magnetic susceptibility (κLF, total organic carbon content (TOC and the chemical index of alteration (CIA; Minyuk et al., 2007, revealed that all stages back to marine isotope stage (MIS 10 and most of the substages are clearly reflected in the pattern derived from the cluster analysis.

  19. Patterns of comorbidity in community-dwelling older people hospitalised for fall-related injury: A cluster analysis

    Directory of Open Access Journals (Sweden)

    Finch Caroline F

    2011-08-01

    Full Text Available Abstract Background Community-dwelling older people aged 65+ years sustain falls frequently; these can result in physical injuries necessitating medical attention including emergency department care and hospitalisation. Certain health conditions and impairments have been shown to contribute independently to the risk of falling or experiencing a fall injury, suggesting that individuals with these conditions or impairments should be the focus of falls prevention. Since older people commonly have multiple conditions/impairments, knowledge about which conditions/impairments coexist in at-risk individuals would be valuable in the implementation of a targeted prevention approach. The objective of this study was therefore to examine the prevalence and patterns of comorbidity in this population group. Methods We analysed hospitalisation data from Victoria, Australia's second most populous state, to estimate the prevalence of comorbidity in patients hospitalised at least once between 2005-6 and 2007-8 for treatment of acute fall-related injuries. In patients with two or more comorbid conditions (multicomorbidity we used an agglomerative hierarchical clustering method to cluster comorbidity variables and identify constellations of conditions. Results More than one in four patients had at least one comorbid condition and among patients with comorbidity one in three had multicomorbidity (range 2-7. The prevalence of comorbidity varied by gender, age group, ethnicity and injury type; it was also associated with a significant increase in the average cumulative length of stay per patient. The cluster analysis identified five distinct, biologically plausible clusters of comorbidity: cardiopulmonary/metabolic, neurological, sensory, stroke and cancer. The cardiopulmonary/metabolic cluster was the largest cluster among the clusters identified. Conclusions The consequences of comorbidity clustering in terms of falls and/or injury outcomes of hospitalised patients

  20. 改进的蚁群算法与凝聚相结合的关键帧提取%Key frame extraction based on improved ant algorithm and agglomerative

    Institute of Scientific and Technical Information of China (English)

    张建明; 刘海燕; 孙淑敏

    2013-01-01

    The key frame extraction is very important to content-based video retrieval. In order to extract key frame efficiently from different types of video, an efficient method for key frame extraction based on improved ant algorithm and agglomerative is proposed. An improved ant algorithm is applied to the histogram differences and texture of video shot self-organized, and obtains an initial clustering result; agglomerative is conducted to optimize the initial clustering result and then a final clustering result is obtained; the center frame of each clustering is extracted as the key frame. The experiment result shows that, the key frames extracted by using this algorithm can adequately express the primary content of the video, and proper quantities of key frames also can be extracted according to the change of the video content.%关键帧提取技术,对基于内容的视频检索有着重要的作用.为了从不同类型的视频中有效地提取关键帧,提出了改进的蚁群算法与凝聚相结合的关键帧提取算法.该方法提取视频中每帧的颜色与边缘特征向量,利用改进的蚁群算法自组织地对颜色和边缘特征向量进行聚类,得到初始聚类.通过凝聚算法对初始聚类进行优化,得到最终聚类.提取每类中距离聚类中心最近的向量,将其对应帧作为关键帧.实验结果表明:使用该算法提取的关键帧不仅可以充分表达出视频的主要内容,而且可以根据视频内容的变化提取出适当数量的关键帧.

  1. 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.

  2. 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.

  3. 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.

  4. 基于稀疏Parzen窗密度估计的快速自适应相似度聚类方法%Fast Adaptive Similarity-based Clustering Using Sparse Parzen Window Density Estimation

    Institute of Scientific and Technical Information of China (English)

    钱鹏江; 王士同; 邓赵红

    2011-01-01

    相似度聚类方法(Similarity-based clustering method,SCM)因其简单易实现和具有鲁棒性而广受关注.但由于内含相似度聚类算法(Similarity clustering algorithm,SCA)的高时间复杂度和凝聚型层次聚类(Agglomerative hierarchicalclustering,AHC)的高空间复杂度,SCM不适用大数据集场合.本文首先发现了 SCM和核密度估计问题的本质联系,并以此入手,通过快速压缩集密度估计器(Fast reduced set density estimator,FRSDE)和基于图的松弛聚类(Graph-based relaxed clustering,GRC)算法提出了快速自适应相似度聚类方法(Fast adaptive similarity-based clustering method,FASCM).相比于原SCM,该方法的主要优点是:1)其总体渐近时间复杂度与样本容量呈线性关系;2)不依赖于人工经验的干预,具有了自适应性.由此,FASCM适用于大数据集环境.该方法的有效性在图像分割应用中进行了验证.%Similarity-based clustering method (SCM) has received much attention because it is robust and can be implemented simply and easily.However, because of its high time complexity of the embedded similarity clustering algorithm (SCA) and high space complexity of the embedded agglomerative hierarchical clustering (AHC), SCM is impractical for large data sets.In this paper, the relationship is revealed between SCM and the kernel density estimation of samples, a novel fast adaptive similarity-based clustering method (FASCM) is accordingly proposed by adopting fast reduced set density estimator (FRSDE) and graph-based relaxed clustering (GRC).The distinctive advantages of FMSSC over MSSC exist in: 1) its asymptotic linear time complexity with the data size; 2) independent on artificial experience and its adaptability.Thus, FASCM is practical for large datasets.Its effectiveness has also been demonstrated in image segmentation examples.

  5. 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.

  6. 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...

  7. 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。

  8. 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.

  9. Random sequential renormalization and agglomerative percolation in networks: application to Erdös-Rényi and scale-free graphs.

    Science.gov (United States)

    Bizhani, Golnoosh; Grassberger, Peter; Paczuski, Maya

    2011-12-01

    We study the statistical behavior under random sequential renormalization (RSR) of several network models including Erdös-Rényi (ER) graphs, scale-free networks, and an annealed model related to ER graphs. In RSR the network is locally coarse grained by choosing at each renormalization step a node at random and joining it to all its neighbors. Compared to previous (quasi-)parallel renormalization methods [Song et al., Nature (London) 433, 392 (2005)], RSR allows a more fine-grained analysis of the renormalization group (RG) flow and unravels new features that were not discussed in the previous analyses. In particular, we find that all networks exhibit a second-order transition in their RG flow. This phase transition is associated with the emergence of a giant hub and can be viewed as a new variant of percolation, called agglomerative percolation. We claim that this transition exists also in previous graph renormalization schemes and explains some of the scaling behavior seen there. For critical trees it happens as N/N(0) → 0 in the limit of large systems (where N(0) is the initial size of the graph and N its size at a given RSR step). In contrast, it happens at finite N/N(0) in sparse ER graphs and in the annealed model, while it happens for N/N(0) → 1 on scale-free networks. Critical exponents seem to depend on the type of the graph but not on the average degree and obey usual scaling relations for percolation phenomena. For the annealed model they agree with the exponents obtained from a mean-field theory. At late times, the networks exhibit a starlike structure in agreement with the results of Radicchi et al. [Phys. Rev. Lett. 101, 148701 (2008)]. While degree distributions are of main interest when regarding the scheme as network renormalization, mass distributions (which are more relevant when considering "supernodes" as clusters) are much easier to study using the fast Newman-Ziff algorithm for percolation, allowing us to obtain very high statistics.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Relationship between the magnitude of symptoms and the quality of life: a cluster analysis of lung cancer patients in Brazil.

    Science.gov (United States)

    Franceschini, Juliana; Jardim, José Roberto; Fernandes, Ana Luisa Godoy; Jamnik, Sérgio; Santoro, Ilka Lopes

    2013-01-01

    Lung cancer patients often experience profound physical and psychosocial changes as a result of disease progression or treatment side effects. Fatigue, pain, dyspnea, depression, and sleep disturbances appear to be the most common symptoms in such patients. The objective of the present study was to examine the prevalence of symptoms in lung cancer patients in order to identify subgroups (clusters) of patients, grouped according to the magnitude of the symptoms, as well as to compare the quality of life among the identified subgroups. A cross-sectional study involving agglomerative hierarchical clustering. A total of 50 lung cancer patients were evaluated in terms of their demographic characteristics and their scores on three quality of life questionnaires, namely the 30-item European Organization for Research and Treatment of Cancer Core Quality of Life Questionnaire (EORTC QLQ-C30), the Functional Assessment of Cancer Therapy-Lung, and the Medical Outcomes Study 36-item Short-form Survey. The cluster analysis took into account the magnitude of the most prevalent symptoms as assessed by the EORTC QLQ-C30 symptom scale scores; those symptoms were fatigue, pain, dyspnea, and insomnia. Three clusters (subgroups)_of patients were identified on the basis of the magnitude of the four most prevalent symptoms. The three subgroups of patients were as follows: patients with mild symptoms (n = 30; 60%); patients with moderate symptoms (n = 14; 28%); and patients with severe symptoms (n = 6; 12%). The subgroup of patients with severe symptoms had the worst quality of life, as assessed by the total scores and by the integrated domains of all three instruments. This study highlights the importance of symptom cluster assessment as an important tool to assess the quality of life of patients with chronic diseases, such as lung cancer.

  15. 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

  16. 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.

  17. 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

  18. Molecular Clustering Interrelationships and Carbohydrate Conformation in Hull and Seeds Among Barley Cultivars

    Energy Technology Data Exchange (ETDEWEB)

    N Liu; P Yu

    2011-12-31

    The objective of this study was to use molecular spectral analyses with the diffuse reflectance Fourier transform infrared spectroscopy (DRIFT) bioanlytical technique to study carbohydrate conformation features, molecular clustering and interrelationships in hull and seed among six barley cultivars (AC Metcalfe, CDC Dolly, McLeod, CDC Helgason, CDC Trey, CDC Cowboy), which had different degradation kinetics in rumen. The molecular structure spectral analyses in both hull and seed involved the fingerprint regions of ca. 1536-1484 cm{sup -1} (attributed mainly to aromatic lignin semicircle ring stretch), ca. 1293-1212 cm{sup -1} (attributed mainly to cellulosic compounds in the hull), ca. 1269-1217 cm{sup -1} (attributed mainly to cellulosic compound in the seeds), and ca. 1180-800 cm{sup -1} (attributed mainly to total CHO C-O stretching vibrations) together with an agglomerative hierarchical cluster (AHCA) and principal component spectral analyses (PCA). The results showed that the DRIFT technique plus AHCA and PCA molecular analyses were able to reveal carbohydrate conformation features and identify carbohydrate molecular structure differences in both hull and seeds among the barley varieties. The carbohydrate molecular spectral analyses at the region of ca. 1185-800 cm{sup -1} together with the AHCA and PCA were able to show that the barley seed inherent structures exhibited distinguishable differences among the barley varieties. CDC Helgason had differences from AC Metcalfe, MeLeod, CDC Cowboy and CDC Dolly in carbohydrate conformation in the seed. Clear molecular cluster classes could be distinguished and identified in AHCA analysis and the separate ellipses could be grouped in PCA analysis. But CDC Helgason had no distinguished differences from CDC Trey in carbohydrate conformation. These carbohydrate conformation/structure difference could partially explain why the varieties were different in digestive behaviors in animals. The molecular spectroscopy

  19. 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.

  20. 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

  1. 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

  2. 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.

  3. 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.

  4. Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

    Science.gov (United States)

    Lensink, Marc F.; Petta, Andrea; Serra, Luigi; Scarano, Vittorio; Cavallo, Luigi; Oliva, Romina

    2016-01-01

    Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers’ performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked

  5. Introducing a Clustering Step in a Consensus Approach for the Scoring of Protein-Protein Docking Models

    KAUST Repository

    Chermak, Edrisse

    2016-11-15

    Correctly scoring protein-protein docking models to single out native-like ones is an open challenge. It is also an object of assessment in CAPRI (Critical Assessment of PRedicted Interactions), the community-wide blind docking experiment. We introduced in the field the first pure consensus method, CONSRANK, which ranks models based on their ability to match the most conserved contacts in the ensemble they belong to. In CAPRI, scorers are asked to evaluate a set of available models and select the top ten ones, based on their own scoring approach. Scorers\\' performance is ranked based on the number of targets/interfaces for which they could provide at least one correct solution. In such terms, blind testing in CAPRI Round 30 (a joint prediction round with CASP11) has shown that critical cases for CONSRANK are represented by targets showing multiple interfaces or for which only a very small number of correct solutions are available. To address these challenging cases, CONSRANK has now been modified to include a contact-based clustering of the models as a preliminary step of the scoring process. We used an agglomerative hierarchical clustering based on the number of common inter-residue contacts within the models. Two criteria, with different thresholds, were explored in the cluster generation, setting either the number of common contacts or of total clusters. For each clustering approach, after selecting the top (most populated) ten clusters, CONSRANK was run on these clusters and the top-ranked model for each cluster was selected, in the limit of 10 models per target. We have applied our modified scoring approach, Clust-CONSRANK, to SCORE_SET, a set of CAPRI scoring models made recently available by CAPRI assessors, and to the subset of homodimeric targets in CAPRI Round 30 for which CONSRANK failed to include a correct solution within the ten selected models. Results show that, for the challenging cases, the clustering step typically enriches the ten top ranked

  6. 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.

  7. 两阶段混合粒子群优化聚类%Two-Step Hybrid PSO-Based Clustering Algorithm

    Institute of Scientific and Technical Information of China (English)

    王纵虎; 刘志镜; 陈东辉

    2012-01-01

    In order to solve the problems of the existing PSO (particle swarm optimization) K-means algorithms, i. e. , their calculation speeds are slow and the clustering results are unstable when samples have a high dimension, some high-quality sub-clusters were generated by hierarchical agglomerative clustering. These sub-clusters were used as the search space of candidate centroids of the PSO K-means. In order to reduce the computational complexity when the dimension of a sample is high, a simplified particle encoding method was proposed. In addition, chaotic idea was introduced to keep the diversity of particle swarm to avoid premature. By two-step hybrid clustering the advantages of the hierarchical clustering, the partitioning clustering and the PSO were combined. The experimental results on several UCI data sets show that compared with the best results of several contrastive algorithms, the purity of its clustering result increases by 1% to 8% and the consuming time reduces by 50% at least.%为解决数据集样本维数较高时已有粒子群优化K均值算法计算速度较慢且聚类结果不稳定的问题,利用第1阶段聚类层次凝聚聚类获得准确率较高的子簇集合,作为粒子群优化K均值聚类算法初始聚类中心的搜索空间,进行第2阶段聚类.提出了一种简化的粒子编码方法,以减小样本维数对计算复杂度的影响;引入混沌的思想,以保持粒子种群的多样性,从而避免粒子群优化算法可能出现的早熟现象.通过两阶段聚类,有效地融合了粒子群优化、层次聚类与划分聚类算法的优点.在多个UCI数据集上的聚类结果表明,与几种对比算法聚类结果的最优值相比,其纯度分别提高了1%~8%,且耗时减少50%以上.

  8. 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...

  9. 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.

  10. 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

  11. 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...

  12. 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%.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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, ...

  18. 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.

  19. Waveforms clustering and single-station location of microearthquake multiplets recorded in the northern Sicilian offshore region

    Science.gov (United States)

    D'Alessandro, Antonino; Mangano, Giorgio; D'Anna, Giuseppe; Luzio, Dario

    2013-09-01

    In 2009 December, the OBSLab-INGV (Istituto Nazionale di Geofisica e Vulcanologia) deployed an Ocean Bottom Seismometer with Hydrophone (OBS/H) near the epicentral area of the main shock of the Palermo seismic sequence of 2002. The monitoring activity had a total duration of about 8 months. During this experiment, the OBS/H recorded 247 very local microearthquakes, whose local magnitude is between -0.5 and 2.5 and TS - TP delay time between 0.2 and 5 s, almost all of which were undetected by the Italian National Seismic Network. This local microseismicity has been analysed using an innovative clustering technique that exploits the similarity between the waveforms generated by different events. The clustering technique implemented, based on hierarchical agglomerative algorithms, nearest neighbour technique and dendrogram representation, allowed us to identify nine distinct multiplets characterized by a high degree of similarity between the waveforms. The microevents were located through an improved single-station location (SSL) technique based on the polarization analysis of the 3C signals and on the estimation of the TS - TP time. In the new SSL technique, an unbiased covariance matrix was defined and a ray tracer-based determination of the epicentral distance and hypocentral depth was proposed. All the multiplets were generated by events with hypocentres that were very close to each other. However, not all the identified clusters are also clustered in the time-magnitude domain. It was also observed that some multiplets have clouds of hypocentres overlapping each other. These clusters, indistinguishable without the application of a waveforms clustering technique, show differences in the waveforms that must be attributed to differences in the focal mechanisms which generated the waveforms. The local seismic events recorded are typical of a seismicity generated by a volume characterized by a highly complex fracturing pattern and by an important role in the dynamics

  20. 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

  1. 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.

  2. 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

  3. 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.

  4. 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 .

  5. 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...

  6. 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...

  7. 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...

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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....

  14. 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...

  15. 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.

  16. [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.

  17. High-Dimensional Bayesian Clustering with Variable Selection: The R Package bclust

    Directory of Open Access Journals (Sweden)

    Vahid Partovi Nia

    2012-04-01

    Full Text Available The R package bclust is useful for clustering high-dimensional continuous data. The package uses a parametric spike-and-slab Bayesian model to downweight the effect of noise variables and to quantify the importance of each variable in agglomerative clustering. We take advantage of the existence of closed-form marginal distributions to estimate the model hyper-parameters using empirical Bayes, thereby yielding a fully automatic method. We discuss computational problems arising in implementation of the procedure and illustrate the usefulness of the package through examples.

  18. Seismotectonic Implications Of Clustered Regional GPS Velocities In The San Francisco Bay Region, California

    Science.gov (United States)

    Graymer, R. W.; Simpson, R.

    2012-12-01

    We have used a hierarchical agglomerative clustering algorithm with Euclidean distance and centroid linkage, applied to continuous GPS observations for the Bay region available from the U.S. Geological Survey website. This analysis reveals 4 robust, spatially coherent clusters that coincide with 4 first-order structural blocks separated by 3 major fault systems: San Andreas (SA), Southern/Central Calaveras-Hayward-Rodgers Creek-Maacama (HAY), and Northern Calaveras-Concord-Green Valley-Berryessa-Bartlett Springs (NCAL). Because observations seaward of the San Gregorio (SG) fault are few in number, the cluster to the west of SA may actually contain 2 major structural blocks not adequately resolved: the Pacific plate to the west of the northern SA and a Peninsula block between the Peninsula SA and the SG fault. The average inter-block velocities are 11, 10, and 9 mm/yr across SA, HAY, and NCAL respectively. There appears to be a significant component of fault-normal compression across NCAL, whereas SA and HAY faults appear to be, on regional average, purely strike-slip. The velocities for the Sierra Nevada - Great Valley (SNGV) block to the west of NCAL are impressive in their similarity. The cluster of these velocities in a velocity plot forms a tighter grouping compared with the groupings for the other cluster blocks, suggesting a more rigid behavior for this block than the others. We note that for 4 clusters, none of the 3 cluster boundaries illuminate geologic structures other than north-northwest trending dominantly strike-slip faults, so plate motion is not accommodated by large-scale fault-parallel compression or extension in the region or by significant plastic deformation , at least over the time span of the GPS observations. Complexities of interseismic deformation of the upper crust do not allow simple application of inter-block velocities as long-term slip rates on bounding faults. However, 2D dislocation models using inter-block velocities and typical

  19. 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...

  20. 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.

  1. A model-independent approach to infer hierarchical codon substitution dynamics.

    Science.gov (United States)

    Görnerup, Olof; Jacobi, Martin Nilsson

    2010-04-23

    Codon substitution constitutes a fundamental process in molecular biology that has been studied extensively. However, prior studies rely on various assumptions, e.g. regarding the relevance of specific biochemical properties, or on conservation criteria for defining substitution groups. Ideally, one would instead like to analyze the substitution process in terms of raw dynamics, independently of underlying system specifics. In this paper we propose a method for doing this by identifying groups of codons and amino acids such that these groups imply closed dynamics. The approach relies on recently developed spectral and agglomerative techniques for identifying hierarchical organization in dynamical systems. We have applied the techniques on an empirically derived Markov model of the codon substitution process that is provided in the literature. Without system specific knowledge of the substitution process, the techniques manage to "blindly" identify multiple levels of dynamics; from amino acid substitutions (via the standard genetic code) to higher order dynamics on the level of amino acid groups. We hypothesize that the acquired groups reflect earlier versions of the genetic code. The results demonstrate the applicability of the techniques. Due to their generality, we believe that they can be used to coarse grain and identify hierarchical organization in a broad range of other biological systems and processes, such as protein interaction networks, genetic regulatory networks and food webs.

  2. A model-independent approach to infer hierarchical codon substitution dynamics

    Directory of Open Access Journals (Sweden)

    Jacobi Martin

    2010-04-01

    Full Text Available Abstract Background Codon substitution constitutes a fundamental process in molecular biology that has been studied extensively. However, prior studies rely on various assumptions, e.g. regarding the relevance of specific biochemical properties, or on conservation criteria for defining substitution groups. Ideally, one would instead like to analyze the substitution process in terms of raw dynamics, independently of underlying system specifics. In this paper we propose a method for doing this by identifying groups of codons and amino acids such that these groups imply closed dynamics. The approach relies on recently developed spectral and agglomerative techniques for identifying hierarchical organization in dynamical systems. Results We have applied the techniques on an empirically derived Markov model of the codon substitution process that is provided in the literature. Without system specific knowledge of the substitution process, the techniques manage to "blindly" identify multiple levels of dynamics; from amino acid substitutions (via the standard genetic code to higher order dynamics on the level of amino acid groups. We hypothesize that the acquired groups reflect earlier versions of the genetic code. Conclusions The results demonstrate the applicability of the techniques. Due to their generality, we believe that they can be used to coarse grain and identify hierarchical organization in a broad range of other biological systems and processes, such as protein interaction networks, genetic regulatory networks and food webs.

  3. An alternative hierarchical organization of the mental disorders of the DSM-IV.

    Science.gov (United States)

    Flanagan, Elizabeth H; Keeley, Jared; Blashfield, Roger K

    2008-08-01

    With the approaching publication of the fifth edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM), alternative organizations of the DSM (4th ed.; DSM-IV; American Psychiatric Association, 1994) categories have been proposed. This article compares several published alternative organizations to clinicians' organization of the DSM-IV categories. As demonstrations of their organization of DSM-IV categories, psychologists and psychiatrists sorted 66 DSM-IV diagnostic categories into groups of similar diagnoses and then made progressively larger and smaller groups of diagnoses or placed similar groups next to each other on a table. Hierarchical agglomerative data analysis of clinicians' individual sortings showed that clinicians retained many lower level DSM-IV categories (e.g., anxiety disorders, mood disorders), but not the higher level DSM-IV categories (e.g., Axis I vs. Axis II). Instead, at the highest hierarchical level, clinicians' categories resembled the structure of the first edition of the DSM (American Psychiatric Association, 1952), which followed clinicians' diagnostic decision-making scheme, dividing mental disorders into organic versus nonorganic and then psychotic versus neurotic disorders. At minimum, these data suggest a DSM organization that makes sense to clinicians.

  4. 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.

  5. 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.

  6. 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...

  7. 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.

  8. 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.

  9. 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

  10. 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.

  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. 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.

  13. 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...

  14. 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...

  15. 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,

  16. 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.

  17. 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).

  18. Agglomerative Economies’Effects on Resources Allocation in Zhejiang Province%浙江省集聚经济类型的资源配置效应分析

    Institute of Scientific and Technical Information of China (English)

    白永平; 王培安

    2012-01-01

    集聚经济现象作为经济领域中的热点问题始终以来备受关注,但是从资源配置角度的研究却非常有限。研究结合集聚经济的时空属性,采用Clusterseer、Geoda和Sam计量分析软件,对浙江省2001年至2010年70个市(县)的集聚经济水平进行了多尺度的扫描统计量和空间自相关分析。并从城市化集聚经济和地方化集聚经济类型入手,分别比较了区域资源要素流的聚集规模和聚集范围。进而得出如下结论:①集聚经济在资源配置方面具有显著的效用;②不同的资源要素集聚的强度和范围各不相同;③城市化经济和地方化经济在资源配置方面具有显著的差异性,城市化经济的资源配置效应显著高于地方化经济;④城市化和地方化集聚经济交互作用形成的集聚经济类型能够最大限度地实现资源优化配置的目的。基于上述分析,提出了调控集聚经济的重大意义以及相应的政策建议。%As a hot issue in the economic field, the phenomenon of agglomerative economy has always been paid much attention. At present, the correlation studies on resources allocation at home and abroad are characterized by multi-factor and multi-scale. In summary, they are mainly the micro study with one factor as the core and the macro study with regional space as the core. Studies on resources allocation from the perspective of agglomerative economies are of significant meanings, but this kind of researches are still limited Since aggregation appears a high degree of scale sensitivity, the agglomerative economies’ effect on resources allocation not only has remarkable spatial dependence, but also has obvious time characteristics. Therefore, combined with the spatial-temporal property of agglomerative economies, the research has adopted the quantitative analysis software Geoda 9.5 and Sam 4.0 to make multi-scale spatial autocorrelation analysis on the levels of agglomerative economies in

  19. Quasi-Likelihood Techniques in a Logistic Regression Equation for Identifying Simulium damnosum s.l. Larval Habitats Intra-cluster Covariates in Togo.

    Science.gov (United States)

    Jacob, Benjamin G; Novak, Robert J; Toe, Laurent; Sanfo, Moussa S; Afriyie, Abena N; Ibrahim, Mohammed A; Griffith, Daniel A; Unnasch, Thomas R

    2012-01-01

    The standard methods for regression analyses of clustered riverine larval habitat data of Simulium damnosum s.l. a major black-fly vector of Onchoceriasis, postulate models relating observational ecological-sampled parameter estimators to prolific habitats without accounting for residual intra-cluster error correlation effects. Generally, this correlation comes from two sources: (1) the design of the random effects and their assumed covariance from the multiple levels within the regression model; and, (2) the correlation structure of the residuals. Unfortunately, inconspicuous errors in residual intra-cluster correlation estimates can overstate precision in forecasted S.damnosum s.l. riverine larval habitat explanatory attributes regardless how they are treated (e.g., independent, autoregressive, Toeplitz, etc). In this research, the geographical locations for multiple riverine-based S. damnosum s.l. larval ecosystem habitats sampled from 2 pre-established epidemiological sites in Togo were identified and recorded from July 2009 to June 2010. Initially the data was aggregated into proc genmod. An agglomerative hierarchical residual cluster-based analysis was then performed. The sampled clustered study site data was then analyzed for statistical correlations using Monthly Biting Rates (MBR). Euclidean distance measurements and terrain-related geomorphological statistics were then generated in ArcGIS. A digital overlay was then performed also in ArcGIS using the georeferenced ground coordinates of high and low density clusters stratified by Annual Biting Rates (ABR). This data was overlain onto multitemporal sub-meter pixel resolution satellite data (i.e., QuickBird 0.61m wavbands ). Orthogonal spatial filter eigenvectors were then generated in SAS/GIS. Univariate and non-linear regression-based models (i.e., Logistic, Poisson and Negative Binomial) were also employed to determine probability distributions and to identify statistically significant parameter

  20. 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....

  1. 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.

  2. 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...

  3. 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.

  4. 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.

  5. 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.

  6. 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...

  7. Segmenting Student Markets with a Student Satisfaction and Priorities Survey.

    Science.gov (United States)

    Borden, Victor M. H.

    1995-01-01

    A market segmentation analysis of 872 university students compared 2 hierarchical clustering procedures for deriving market segments: 1 using matching-type measures and an agglomerative clustering algorithm, and 1 using the chi-square based automatic interaction detection. Results and implications for planning, evaluating, and improving academic…

  8. Segmenting Student Markets with a Student Satisfaction and Priorities Survey.

    Science.gov (United States)

    Borden, Victor M. H.

    1995-01-01

    A market segmentation analysis of 872 university students compared 2 hierarchical clustering procedures for deriving market segments: 1 using matching-type measures and an agglomerative clustering algorithm, and 1 using the chi-square based automatic interaction detection. Results and implications for planning, evaluating, and improving academic…

  9. 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...

  10. 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.

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. 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.

  19. 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 ...

  20. Use of multiple cluster analysis methods to explore the validity of a community outcomes concept map.

    Science.gov (United States)

    Orsi, Rebecca

    2017-02-01

    Concept mapping is now a commonly-used technique for articulating and evaluating programmatic outcomes. However, research regarding validity of knowledge and outcomes produced with concept mapping is sparse. The current study describes quantitative validity analyses using a concept mapping dataset. We sought to increase the validity of concept mapping evaluation results by running multiple cluster analysis methods and then using several metrics to choose from among solutions. We present four different clustering methods based on analyses using the R statistical software package: partitioning around medoids (PAM), fuzzy analysis (FANNY), agglomerative nesting (AGNES) and divisive analysis (DIANA). We then used the Dunn and Davies-Bouldin indices to assist in choosing a valid cluster solution for a concept mapping outcomes evaluation. We conclude that the validity of the outcomes map is high, based on the analyses described. Finally, we discuss areas for further concept mapping methods research.

  1. 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...

  2. 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...

  3. 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

  4. 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...

  5. 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.

  6. 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.

  7. 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.

  8. 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...

  9. 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.

  10. 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) ...

  11. 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.

  12. 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.

  13. 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

  14. 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.

  15. 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.

  16. 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.

  17. 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.

  18. Object-Oriented Image Clustering Method Using UAS Photogrammetric Imagery

    Science.gov (United States)

    Lin, Y.; Larson, A.; Schultz-Fellenz, E. S.; Sussman, A. J.; Swanson, E.; Coppersmith, R.

    2016-12-01

    Unmanned Aerial Systems (UAS) have been used widely as an imaging modality to obtain remotely sensed multi-band surface imagery, and are growing in popularity due to their efficiency, ease of use, and affordability. Los Alamos National Laboratory (LANL) has employed the use of UAS for geologic site characterization and change detection studies at a variety of field sites. The deployed UAS equipped with a standard visible band camera to collect imagery datasets. Based on the imagery collected, we use deep sparse algorithmic processing to detect and discriminate subtle topographic features created or impacted by subsurface activities. In this work, we develop an object-oriented remote sensing imagery clustering method for land cover classification. To improve the clustering and segmentation accuracy, instead of using conventional pixel-based clustering methods, we integrate the spatial information from neighboring regions to create super-pixels to avoid salt-and-pepper noise and subsequent over-segmentation. To further improve robustness of our clustering method, we also incorporate a custom digital elevation model (DEM) dataset generated using a structure-from-motion (SfM) algorithm together with the red, green, and blue (RGB) band data for clustering. In particular, we first employ an agglomerative clustering to create an initial segmentation map, from where every object is treated as a single (new) pixel. Based on the new pixels obtained, we generate new features to implement another level of clustering. We employ our clustering method to the RGB+DEM datasets collected at the field site. Through binary clustering and multi-object clustering tests, we verify that our method can accurately separate vegetation from non-vegetation regions, and are also able to differentiate object features on the surface.

  19. 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.

  20. 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.  

  1. 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.

  2. 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.

  3. Biogeographic classification of the Caspian Sea

    DEFF Research Database (Denmark)

    Fendereski, F.; Vogt, M.; Payne, Mark

    2014-01-01

    using the Hierarchical Agglomerative Clustering (HAC) method. From an initial set of 12 potential physical variables, 6 independent variables were selected for the classification algorithm, i.e., sea surface temperature (SST), bathymetry, sea ice, seasonal variation of sea surface salinity (DSSS), total...... confirms the relevance of the ecoregions as proxies for habitats with common biological characteristics....

  4. 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...

  5. 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...

  6. 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.

  7. 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).

  8. 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

  9. 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.

  10. 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'.

  11. 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.

  12. 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...

  13. 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.

  14. 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…

  15. 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…

  16. 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

  17. 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.

  18. 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.

  19. 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.

  20. 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.

  1. 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.

  2. 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…

  3. 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.

  4. 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 ...

  5. 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.

  6. 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...

  7. 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...

  8. 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...

  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. 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...

  15. 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.

  16. 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.

  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

    2016-01-01

    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

  18. 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.

  19. 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.

  20. 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.

  1. 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

  2. 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.

  3. 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 ...

  4. 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.

  5. 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…

  6. 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

  7. 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 ...

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. 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.

  13. 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.

  14. Quasar Evolution Driven by Galaxy Encounters in Hierarchical Structures

    CERN Document Server

    Menci, N; Fontana, A; Giallongo, E; Poli, F; Vittorini, V

    2003-01-01

    We link the evolution of the galaxies in the hierarchical clustering scenario with the changing accretion rates of cold gas onto the central massive black holes that power the quasars. We base on galaxy interactions as main triggers of accretion; the related scaling laws are taken up from Cavaliere & Vittorini (2000), and grafted to a semi-analytic code for galaxy formation. As a result, at high $z$ the protogalaxies grow rapidly by hierarchical merging; meanwhile, much fresh gas is imported and also destabilized, so the holes are fueled at their full Eddington rates. At lower $z$ the galactic dynamical events are mostly encounters in hierarchically growing groups; now the refueling peters out, as the residual gas is exhausted while the destabilizing encounters dwindle. So, with no parameter tuning other than needed for stellar observables, our model uniquely produces at $z>3$ a rise, and at $z\\lesssim 2.5 $ a decline of the bright quasar population as steep as observed. In addition, our results closely f...

  15. Hierarchical organization of brain functional network during visual task

    CERN Document Server

    Zhuo, Zhao; Fu, Zhong-Qian; Zhang, Jie

    2011-01-01

    In this paper, the brain functional networks derived from high-resolution synchronous EEG time series during visual task are generated by calculating the phase synchronization among the time series. The hierarchical modular organizations of these networks are systematically investigated by the fast Girvan-Newman algorithm. At the same time, the spatially adjacent electrodes (corresponding to EEG channels) are clustered into functional groups based on anatomical parcellation of brain cortex, and this clustering information are compared to that of the functional network. The results show that the modular architectures of brain functional network are in coincidence with that from the anatomical structures over different levels of hierarchy, which suggests that population of neurons performing the same function excite and inhibit in identical rhythms. The structure-function relationship further reveals that the correlations among EEG time series in the same functional group are much stronger than those in differe...

  16. Hierarchical Control for Smart Grids

    DEFF Research Database (Denmark)

    Trangbæk, K; Bendtsen, Jan Dimon; Stoustrup, Jakob

    2011-01-01

    This paper deals with hierarchical model predictive control (MPC) of smart grid systems. The design consists of a high level MPC controller, a second level of so-called aggregators, which reduces the computational and communication-related load on the high-level control, and a lower level...... of autonomous consumers. The control system is tasked with balancing electric power production and consumption within the smart grid, and makes active use of the flexibility of a large number of power producing and/or power consuming units. The objective is to accommodate the load variation on the grid, arising...

  17. Functional Clustering Algorithm for High-Dimensional Proteomics Data

    Directory of Open Access Journals (Sweden)

    Halima Bensmail

    2005-01-01

    Full Text Available Clustering proteomics data is a challenging problem for any traditional clustering algorithm. Usually, the number of samples is largely smaller than the number of protein peaks. The use of a clustering algorithm which does not take into consideration the number of features of variables (here the number of peaks is needed. An innovative hierarchical clustering algorithm may be a good approach. We propose here a new dissimilarity measure for the hierarchical clustering combined with a functional data analysis. We present a specific application of functional data analysis (FDA to a high-throughput proteomics study. The high performance of the proposed algorithm is compared to two popular dissimilarity measures in the clustering of normal and human T-cell leukemia virus type 1 (HTLV-1-infected patients samples.

  18. 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.

  19. Data clustering theory, algorithms, and applications

    CERN Document Server

    Gan, Guojun; Wu, Jianhong

    2007-01-01

    Cluster analysis is an unsupervised process that divides a set of objects into homogeneous groups. This book starts with basic information on cluster analysis, including the classification of data and the corresponding similarity measures, followed by the presentation of over 50 clustering algorithms in groups according to some specific baseline methodologies such as hierarchical, center-based, and search-based methods. As a result, readers and users can easily identify an appropriate algorithm for their applications and compare novel ideas with existing results. The book also provides examples of clustering applications to illustrate the advantages and shortcomings of different clustering architectures and algorithms. Application areas include pattern recognition, artificial intelligence, information technology, image processing, biology, psychology, and marketing. Readers also learn how to perform cluster analysis with the C/C++ and MATLAB® programming languages.

  20. Clusters and Groups of Galaxies : International Meeting

    CERN Document Server

    Giuricin, G; Mezzetti, M

    1984-01-01

    The large-scale structure of the Universe and systems Clusters, and Groups of galaxies are topics like Superclusters, They fully justify the meeting on "Clusters of great interest. and Groups of Galaxies". The topics covered included the spatial distribution and the clustering of galaxies; the properties of Superclusters, Clusters and Groups of galaxies; radio and X-ray observations; the problem of unseen matter; theories concerning hierarchical clustering, pancakes, cluster and galaxy formation and evolution. The meeting was held at the International Center for Theoretical Physics in Trieste (Italy) from September 13 to September 16, 1983. It was attended by about 150 participants from 22 nations who presented 67 invited lectures (il) and contributed papers (cp), and 45 poster papers (pp). The Scientific Organizing Committee consisted of F. Bertola, P. Biermann, A. Cavaliere, N. Dallaporta, D. Gerba1, M. Hack, J . V . Peach, D. Sciama (Chairman), G. Setti, M. Tarenghi. We are particularly indebted to D. Scia...

  1. A Survey of Grid Based Clustering Algorithms

    Directory of Open Access Journals (Sweden)

    MR ILANGO

    2010-08-01

    Full Text Available Cluster Analysis, an automatic process to find similar objects from a database, is a fundamental operation in data mining. A cluster is a collection of data objects that are similar to one another within the same cluster and are dissimilar to the objects in other clusters. Clustering techniques have been discussed extensively in SimilaritySearch, Segmentation, Statistics, Machine Learning, Trend Analysis, Pattern Recognition and Classification [1]. Clustering methods can be classified into i Partitioning methods ii Hierarchical methods iii Density-based methods iv Grid-based methods v Model-based methods. Grid based methods quantize the object space into a finite number of cells (hyper-rectangles and then perform the required operations on the quantized space. The main advantage of Grid based method is its fast processing time which depends on number of cells in each dimension in quantized space. In this research paper, we present some of the grid based methods such as CLIQUE (CLustering In QUEst [2], STING (STatistical INformation Grid [3], MAFIA (Merging of Adaptive Intervals Approach to Spatial Data Mining [4], Wave Cluster [5]and O-CLUSTER (Orthogonal partitioning CLUSTERing [6], as a survey andalso compare their effectiveness in clustering data objects. We also present some of the latest developments in Grid Based methods such as Axis Shifted Grid Clustering Algorithm [7] and Adaptive Mesh Refinement [Wei-Keng Liao etc] [8] to improve the processing time of objects.

  2. Meaningful Clusters

    Energy Technology Data Exchange (ETDEWEB)

    Sanfilippo, Antonio P.; Calapristi, Augustin J.; Crow, Vernon L.; Hetzler, Elizabeth G.; Turner, Alan E.

    2004-05-26

    We present an approach to the disambiguation of cluster labels that capitalizes on the notion of semantic similarity to assign WordNet senses to cluster labels. The approach provides interesting insights on how document clustering can provide the basis for developing a novel approach to word sense disambiguation.

  3. Learning curve estimation in medical devices and procedures: hierarchical modeling.

    Science.gov (United States)

    Govindarajulu, Usha S; Stillo, Marco; Goldfarb, David; Matheny, Michael E; Resnic, Frederic S

    2017-07-30

    In the use of medical device procedures, learning effects have been shown to be a critical component of medical device safety surveillance. To support their estimation of these effects, we evaluated multiple methods for modeling these rates within a complex simulated dataset representing patients treated by physicians clustered within institutions. We employed unique modeling for the learning curves to incorporate the learning hierarchy between institution and physicians and then modeled them within established methods that work with hierarchical data such as generalized estimating equations (GEE) and generalized linear mixed effect models. We found that both methods performed well, but that the GEE may have some advantages over the generalized linear mixed effect models for ease of modeling and a substantially lower rate of model convergence failures. We then focused more on using GEE and performed a separate simulation to vary the shape of the learning curve as well as employed various smoothing methods to the plots. We concluded that while both hierarchical methods can be used with our mathematical modeling of the learning curve, the GEE tended to perform better across multiple simulated scenarios in order to accurately model the learning effect as a function of physician and hospital hierarchical data in the use of a novel medical device. We found that the choice of shape used to produce the 'learning-free' dataset would be dataset specific, while the choice of smoothing method was negligibly different from one another. This was an important application to understand how best to fit this unique learning curve function for hierarchical physician and hospital data. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

  4. Hierarchical Structures in Hypertext Learning Environments

    NARCIS (Netherlands)

    Bezdan, Eniko; Kester, Liesbeth; Kirschner, Paul A.

    2011-01-01

    Bezdan, E., Kester, L., & Kirschner, P. A. (2011, 9 September). Hierarchical Structures in Hypertext Learning Environments. Presentation for the visit of KU Leuven, Open University, Heerlen, The Netherlands.

  5. Star Clusters in the Magellanic Clouds-1: Parameterisation and Classification of 1072 Clusters in the LMC

    Science.gov (United States)

    Nayak, P. K.; Subramaniam, A.; Choudhury, S.; Indu, G.; Sagar, Ram

    2016-08-01

    We have introduced a semi-automated quantitative method to estimate the age and reddening of 1072 star clusters in the Large Magellanic Cloud (LMC) using the Optical Gravitational Lensing Experiment (OGLE) III survey data. This study brings out 308 newly parameterised clusters. In a first of its kind, the LMC clusters are classified into groups based on richness/mass as very poor, poor, moderate and rich clusters, similar to the classification scheme of open clusters in the Galaxy. A major cluster formation episode is found to happen at 125±25 Myr in the inner LMC. The bar region of the LMC appears prominently in the age range 60 - 250 Myr and is found to have a relatively higher concentration of poor and moderate clusters. The eastern and the western ends of the bar are found to form clusters initially, which later propagates to the central part. We demonstrate that there is a significant difference in the distribution of clusters as a function of mass, using a movie based on the propagation (in space and time) of cluster formation in various groups. The importance of including the low mass clusters in the cluster formation history is demonstrated. The catalog with parameters, classification, and cleaned and isochrone fitted CMDs of 1072 clusters, which are available as online material, can be further used to understand the hierarchical formation of clusters in selected regions of the LMC.

  6. 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.

  7. Performance comparison of hierarchical checkpoint protocols grid computing

    Directory of Open Access Journals (Sweden)

    Ndeye Massata NDIAYE

    2012-06-01

    Full Text Available Grid infrastructure is a large set of nodes geographically distributed and connected by a communication. In this context, fault tolerance is a necessity imposed by the distribution that poses a number of problems related to the heterogeneity of hardware, operating systems, networks, middleware, applications, the dynamic resource, the scalability, the lack of common memory, the lack of a common clock, the asynchronous communication between processes. To improve the robustness of supercomputing applications in the presence of failures, many techniques have been developed to provide resistance to these faults of the system. Fault tolerance is intended to allow the system to provide service as specified in spite of occurrences of faults. It appears as an indispensable element in distributed systems. To meet this need, several techniques have been proposed in the literature. We will study the protocols based on rollback recovery. These protocols are classified into two categories: coordinated checkpointing and rollback protocols and log-based independent checkpointing protocols or message logging protocols. However, the performance of a protocol depends on the characteristics of the system, network and applications running. Faced with the constraints of large-scale environments, many of algorithms of the literature showed inadequate. Given an application environment and a system, it is not easy to identify the recovery protocol that is most appropriate for a cluster or hierarchical environment, like grid computing. While some protocols have been used successfully in small scale, they are not suitable for use in large scale. Hence there is a need to implement these protocols in a hierarchical fashion to compare their performance in grid computing. In this paper, we propose hierarchical version of four well-known protocols. We have implemented and compare the performance of these protocols in clusters and grid computing using the Omnet++ simulator

  8. An Approach to Assembly Sequence Plannning Based on Hierarchical Strategy and Genetic Algorithm

    Institute of Scientific and Technical Information of China (English)

    Niu Xinwen; Ding Han; Xiong Youlun

    2001-01-01

    Using group and subassembly cluster methods, the hierarchical structure of a product is.generated automatically, which largely reduces the complexity of planning. Based on genetic algofithn the optimal of assembly sequence of each stracture level can be obtained by sequence-bysequence search. As a result, a better assembly sequence of the product can be generated by combining the assembly sequences of all hierarchical structures, which provides more parallelism and flexibility for assembly operations. An industrial example is solved by this new approach.

  9. Cluster Lenses

    CERN Document Server

    Kneib, Jean-Paul; 10.1007/s00159-011-0047-3

    2012-01-01

    Clusters of galaxies are the most recently assembled, massive, bound structures in the Universe. As predicted by General Relativity, given their masses, clusters strongly deform space-time in their vicinity. Clusters act as some of the most powerful gravitational lenses in the Universe. Light rays traversing through clusters from distant sources are hence deflected, and the resulting images of these distant objects therefore appear distorted and magnified. Lensing by clusters occurs in two regimes, each with unique observational signatures. The strong lensing regime is characterized by effects readily seen by eye, namely, the production of giant arcs, multiple-images, and arclets. The weak lensing regime is characterized by small deformations in the shapes of background galaxies only detectable statistically. Cluster lenses have been exploited successfully to address several important current questions in cosmology: (i) the study of the lens(es) - understanding cluster mass distributions and issues pertaining...

  10. 大连市寿险原保险保费收入预测实证分析——基于最小二乘法的谱系聚类马氏链模型%Empirical analysis of forecast premium income of Dalian primary life insurance——based on least-squares method 's hierarchical clustering and Markov chain model

    Institute of Scientific and Technical Information of China (English)

    王鑫

    2012-01-01

    针对保费收入预测问题,以最小二乘法拟合为依托,基于谱系聚类分析的方法,运用马氏链模型对2008-2011年大连市人寿保险月度原保险保费收入的数据进行实证模拟仿真,采用定量分析的方法对大连市人寿保险月度原保险保费收入进行定性预测,结果表明该方法在进行定性预测时预测结果比较准确。%In view of problems in insurance premium income prediction,based on Least-squares,hierarchical clustering analysis and Markov chain,an empirical simulation research was made of monthly premium income of Dalian's primary life insurance during 2008-2011.Quantitive analysis was made of the same data for qualitative prediction.The results show that this method is fairly accurate in qualitative prediction.

  11. On the unnecessary ubiquity of hierarchical linear modeling.

    Science.gov (United States)

    McNeish, Daniel; Stapleton, Laura M; Silverman, Rebecca D

    2017-03-01

    In psychology and the behavioral sciences generally, the use of the hierarchical linear model (HLM) and its extensions for discrete outcomes are popular methods for modeling clustered data. HLM and its discrete outcome extensions, however, are certainly not the only methods available to model clustered data. Although other methods exist and are widely implemented in other disciplines, it seems that psychologists have yet to consider these methods in substantive studies. This article compares and contrasts HLM with alternative methods including generalized estimating equations and cluster-robust standard errors. These alternative methods do not model random effects and thus make a smaller number of assumptions and are interpreted identically to single-level methods with the benefit that estimates are adjusted to reflect clustering of observations. Situations where these alternative methods may be advantageous are discussed including research questions where random effects are and are not required, when random effects can change the interpretation of regression coefficients, challenges of modeling with random effects with discrete outcomes, and examples of published psychology articles that use HLM that may have benefitted from using alternative methods. Illustrative examples are provided and discussed to demonstrate the advantages of the alternative methods and also when HLM would be the preferred method. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

  12. Application of hybrid clustering using parallel k-means algorithm and DIANA algorithm

    Science.gov (United States)

    Umam, Khoirul; Bustamam, Alhadi; Lestari, Dian

    2017-03-01

    DNA is one of the carrier of genetic information of living organisms. Encoding, sequencing, and clustering DNA sequences has become the key jobs and routine in the world of molecular biology, in particular on bioinformatics application. There are two type of clustering, hierarchical clustering and partitioning clustering. In this paper, we combined two type clustering i.e. K-Means (partitioning clustering) and DIANA (hierarchical clustering), therefore it called Hybrid clustering. Application of hybrid clustering using Parallel K-Means algorithm and DIANA algorithm used to clustering DNA sequences of Human Papillomavirus (HPV). The clustering process is started with Collecting DNA sequences of HPV are obtained from NCBI (National Centre for Biotechnology Information), then performing characteristics extraction of DNA sequences. The characteristics extraction result is store in a matrix form, then normalize this matrix using Min-Max normalization and calculate genetic distance using Euclidian Distance. Furthermore, the hybrid clustering is applied by using implementation of Parallel K-Means algorithm and DIANA algorithm. The aim of using Hybrid Clustering is to obtain better clusters result. For validating the resulted clusters, to get optimum number of clusters, we use Davies-Bouldin Index (DBI). In this study, the result of implementation of Parallel K-Means clustering is data clustered become 5 clusters with minimal IDB value is 0.8741, and Hybrid Clustering clustered data become 13 sub-clusters with minimal IDB values = 0.8216, 0.6845, 0.3331, 0.1994 and 0.3952. The IDB value of hybrid clustering less than IBD value of Parallel K-Means clustering only that perform at 1ts stage. Its means clustering using Hybrid Clustering have the better result to clustered DNA sequence of HPV than perform parallel K-Means Clustering only.

  13. Robust multi-scale clustering of large DNA microarray datasets with the consensus algorithm

    DEFF Research Database (Denmark)

    Grotkjær, Thomas; Winther, Ole; Regenberg, Birgitte

    2006-01-01

    Motivation: Hierarchical and relocation clustering (e.g. K-means and self-organizing maps) have been successful tools in the display and analysis of whole genome DNA microarray expression data. However, the results of hierarchical clustering are sensitive to outliers, and most relocation methods...... analysis by collecting re-occurring clustering patterns in a co-occurrence matrix. The results show that consensus clustering obtained from clustering multiple times with Variational Bayes Mixtures of Gaussians or K-means significantly reduces the classification error rate for a simulated dataset....... The method is flexible and it is possible to find consensus clusters from different clustering algorithms. Thus, the algorithm can be used as a framework to test in a quantitative manner the homogeneity of different clustering algorithms. We compare the method with a number of state-of-the-art clustering...

  14. Tidally Induced Elongation and Alignments of Galaxy Clusters

    CERN Document Server

    Salvador-Solé, E; Salvador-Sole, Eduard; Solanes, Jose M.

    1993-01-01

    We show that tidal interaction among galaxy clusters can account for their observed alignments and very marked elongation and, consequently, that these characteristics of clusters are actually consistent with them being formed in hierarchical clustering. The well-established distribution of projected axial ratios of clusters with richness class $R\\ge 0$ is recovered very satisfactorily by means of a simple model with no free parameters. The main perturbers are relatively rich ($R\\ge 1$) single clusters and/or groups of clusters (superclusters) of a wider richness class ($R\\ge 0$) located within a distance of about 65 $h^{-1}$ Mpc from the perturbed cluster. This makes the proposed scheme be also consistent with all reported alignment effects involving clusters. We find that this tidal interaction is typically in the saturate regime (\\ie the maximum elongation allowed for systems in equilibrium is reached), which explains the very similar intrinsic axial ratio shown by all clusters. Tides would therefore play ...

  15. Discursive Hierarchical Patterning in Economics Cases

    Science.gov (United States)

    Lung, Jane

    2011-01-01

    This paper attempts to apply Lung's (2008) model of the discursive hierarchical patterning of cases to a closer and more specific study of Economics cases and proposes a model of the distinct discursive hierarchical patterning of the same. It examines a corpus of 150 Economics cases with a view to uncovering the patterns of discourse construction.…

  16. A Model of Hierarchical Key Assignment Scheme

    Institute of Scientific and Technical Information of China (English)

    ZHANG Zhigang; ZHAO Jing; XU Maozhi

    2006-01-01

    A model of the hierarchical key assignment scheme is approached in this paper, which can be used with any cryptography algorithm. Besides, the optimal dynamic control property of a hierarchical key assignment scheme will be defined in this paper. Also, our scheme model will meet this property.

  17. Groups possessing extensive hierarchical decompositions

    CERN Document Server

    Januszkiewicz, T; Leary, I J

    2009-01-01

    Kropholler's class of groups is the smallest class of groups which contains all finite groups and is closed under the following operator: whenever $G$ admits a finite-dimensional contractible $G$-CW-complex in which all stabilizer groups are in the class, then $G$ is itself in the class. Kropholler's class admits a hierarchical structure, i.e., a natural filtration indexed by the ordinals. For example, stage 0 of the hierarchy is the class of all finite groups, and stage 1 contains all groups of finite virtual cohomological dimension. We show that for each countable ordinal $\\alpha$, there is a countable group that is in Kropholler's class which does not appear until the $\\alpha+1$st stage of the hierarchy. Previously this was known only for $\\alpha= 0$, 1 and 2. The groups that we construct contain torsion. We also review the construction of a torsion-free group that lies in the third stage of the hierarchy.

  18. Quantum transport through hierarchical structures.

    Science.gov (United States)

    Boettcher, S; Varghese, C; Novotny, M A

    2011-04-01

    The transport of quantum electrons through hierarchical lattices is of interest because such lattices have some properties of both regular lattices and random systems. We calculate the electron transmission as a function of energy in the tight-binding approximation for two related Hanoi networks. HN3 is a Hanoi network with every site having three bonds. HN5 has additional bonds added to HN3 to make the average number of bonds per site equal to five. We present a renormalization group approach to solve the matrix equation involved in this quantum transport calculation. We observe band gaps in HN3, while no such band gaps are observed in linear networks or in HN5. We provide a detailed scaling analysis near the edges of these band gaps.

  19. Hierarchical networks of scientific journals

    CERN Document Server

    Palla, Gergely; Mones, Enys; Pollner, Péter; Vicsek, Tamás

    2015-01-01

    Scientific journals are the repositories of the gradually accumulating knowledge of mankind about the world surrounding us. Just as our knowledge is organised into classes ranging from major disciplines, subjects and fields to increasingly specific topics, journals can also be categorised into groups using various metrics. In addition to the set of topics characteristic for a journal, they can also be ranked regarding their relevance from the point of overall influence. One widespread measure is impact factor, but in the present paper we intend to reconstruct a much more detailed description by studying the hierarchical relations between the journals based on citation data. We use a measure related to the notion of m-reaching centrality and find a network which shows the level of influence of a journal from the point of the direction and efficiency with which information spreads through the network. We can also obtain an alternative network using a suitably modified nested hierarchy extraction method applied ...

  20. Adaptive Sampling in Hierarchical Simulation

    Energy Technology Data Exchange (ETDEWEB)

    Knap, J; Barton, N R; Hornung, R D; Arsenlis, A; Becker, R; Jefferson, D R

    2007-07-09

    We propose an adaptive sampling methodology for hierarchical multi-scale simulation. The method utilizes a moving kriging interpolation to significantly reduce the number of evaluations of finer-scale response functions to provide essential constitutive information to a coarser-scale simulation model. The underlying interpolation scheme is unstructured and adaptive to handle the transient nature of a simulation. To handle the dynamic construction and searching of a potentially large set of finer-scale response data, we employ a dynamic metric tree database. We study the performance of our adaptive sampling methodology for a two-level multi-scale model involving a coarse-scale finite element simulation and a finer-scale crystal plasticity based constitutive law.

  1. 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.

  2. TWO-STAGE CHARACTER CLASSIFICATION : A COMBINED APPROACH OF CLUSTERING AND SUPPORT VECTOR CLASSIFIERS

    NARCIS (Netherlands)

    Vuurpijl, L.; Schomaker, L.

    2000-01-01

    This paper describes a two-stage classification method for (1) classification of isolated characters and (2) verification of the classification result. Character prototypes are generated using hierarchical clustering. For those prototypes known to sometimes produce wrong classification results, a

  3. Hierarchically Nanostructured Materials for Sustainable Environmental Applications

    Science.gov (United States)

    Ren, Zheng; Guo, Yanbing; Liu, Cai-Hong; Gao, Pu-Xian

    2013-11-01

    This article presents a comprehensive overview of the hierarchical nanostructured materials with either geometry or composition complexity in environmental applications. The hierarchical nanostructures offer advantages of high surface area, synergistic interactions and multiple functionalities towards water remediation, environmental gas sensing and monitoring as well as catalytic gas treatment. Recent advances in synthetic strategies for various hierarchical morphologies such as hollow spheres and urchin-shaped architectures have been reviewed. In addition to the chemical synthesis, the physical mechanisms associated with the materials design and device fabrication have been discussed for each specific application. The development and application of hierarchical complex perovskite oxide nanostructures have also been introduced in photocatalytic water remediation, gas sensing and catalytic converter. Hierarchical nanostructures will open up many possibilities for materials design and device fabrication in environmental chemistry and technology.

  4. A neural signature of hierarchical reinforcement learning.

    Science.gov (United States)

    Ribas-Fernandes, José J F; Solway, Alec; Diuk, Carlos; McGuire, Joseph T; Barto, Andrew G; Niv, Yael; Botvinick, Matthew M

    2011-07-28

    Human behavior displays hierarchical structure: simple actions cohere into subtask sequences, which work together to accomplish overall task goals. Although the neural substrates of such hierarchy have been the target of increasing research, they remain poorly understood. We propose that the computations supporting hierarchical behavior may relate to those in hierarchical reinforcement learning (HRL), a machine-learning framework that extends reinforcement-learning mechanisms into hierarchical domains. To test this, we leveraged a distinctive prediction arising from HRL. In ordinary reinforcement learning, reward prediction errors are computed when there is an unanticipated change in the prospects for accomplishing overall task goals. HRL entails that prediction errors should also occur in relation to task subgoals. In three neuroimaging studies we observed neural responses consistent with such subgoal-related reward prediction errors, within structures previously implicated in reinforcement learning. The results reported support the relevance of HRL to the neural processes underlying hierarchical behavior.

  5. Hierarchical Identity-Based Lossy Trapdoor Functions

    CERN Document Server

    Escala, Alex; Libert, Benoit; Rafols, Carla

    2012-01-01

    Lossy trapdoor functions, introduced by Peikert and Waters (STOC'08), have received a lot of attention in the last years, because of their wide range of applications in theoretical cryptography. The notion has been recently extended to the identity-based scenario by Bellare et al. (Eurocrypt'12). We provide one more step in this direction, by considering the notion of hierarchical identity-based lossy trapdoor functions (HIB-LTDFs). Hierarchical identity-based cryptography generalizes identitybased cryptography in the sense that identities are organized in a hierarchical way; a parent identity has more power than its descendants, because it can generate valid secret keys for them. Hierarchical identity-based cryptography has been proved very useful both for practical applications and to establish theoretical relations with other cryptographic primitives. In order to realize HIB-LTDFs, we first build a weakly secure hierarchical predicate encryption scheme. This scheme, which may be of independent interest, is...

  6. Hierarchically nanostructured materials for sustainable environmental applications

    Science.gov (United States)

    Ren, Zheng; Guo, Yanbing; Liu, Cai-Hong; Gao, Pu-Xian

    2013-01-01

    This review presents a comprehensive overview of the hierarchical nanostructured materials with either geometry or composition complexity in environmental applications. The hierarchical nanostructures offer advantages of high surface area, synergistic interactions, and multiple functionalities toward water remediation, biosensing, environmental gas sensing and monitoring as well as catalytic gas treatment. Recent advances in synthetic strategies for various hierarchical morphologies such as hollow spheres and urchin-shaped architectures have been reviewed. In addition to the chemical synthesis, the physical mechanisms associated with the materials design and device fabrication have been discussed for each specific application. The development and application of hierarchical complex perovskite oxide nanostructures have also been introduced in photocatalytic water remediation, gas sensing, and catalytic converter. Hierarchical nanostructures will open up many possibilities for materials design and device fabrication in environmental chemistry and technology. PMID:24790946

  7. Hierarchically Nanostructured Materials for Sustainable Environmental Applications

    Directory of Open Access Journals (Sweden)

    Zheng eRen

    2013-11-01

    Full Text Available This article presents a comprehensive overview of the hierarchical nanostructured materials with either geometry or composition complexity in environmental applications. The hierarchical nanostructures offer advantages of high surface area, synergistic interactions and multiple functionalities towards water remediation, environmental gas sensing and monitoring as well as catalytic gas treatment. Recent advances in synthetic strategies for various hierarchical morphologies such as hollow spheres and urchin-shaped architectures have been reviewed. In addition to the chemical synthesis, the physical mechanisms associated with the materials design and device fabrication have been discussed for each specific application. The development and application of hierarchical complex perovskite oxide nanostructures have also been introduced in photocatalytic water remediation, gas sensing and catalytic converter. Hierarchical nanostructures will open up many possibilities for materials design and device fabrication in environmental chemistry and technology.

  8. Hierarchically Nanoporous Bioactive Glasses for High Efficiency Immobilization of Enzymes

    DEFF Research Database (Denmark)

    He, W.; Min, D.D.; Zhang, X.D.

    2014-01-01

    Bioactive glasses with hierarchical nanoporosity and structures have been heavily involved in immobilization of enzymes. Because of meticulous design and ingenious hierarchical nanostructuration of porosities from yeast cell biotemplates, hierarchically nanostructured porous bioactive glasses can...

  9. Hierarchical structure of the countries based on electricity consumption and economic growth

    Science.gov (United States)

    Kantar, Ersin; Aslan, Alper; Deviren, Bayram; Keskin, Mustafa

    2016-07-01

    We investigate the hierarchical structures of countries based on electricity consumption and economic growth by using the real amounts of their consumption over a certain time period. We use electricity consumption data to detect the topological properties of 64 countries from 1971 to 2008. These countries are divided into three clusters: low income group, middle income group and high income group countries. Firstly, a relationship between electricity consumption and economic growth is investigated by using the concept of hierarchical structure methods (minimal spanning tree (MST) and hierarchical tree (HT)). Secondly, we perform bootstrap techniques to investigate a value of the statistical reliability to the links of the MST. Finally, we use a clustering linkage procedure in order to observe the cluster structure more clearly. The results of the structural topologies of these trees are as follows: (i) we identified different clusters of countries according to their geographical location and economic growth, (ii) we found a strong relation between energy consumption and economic growth for all the income groups considered in this study and (iii) the results are in good agreement with the causal relationship between electricity consumption and economic growth.

  10. Dynamic Hierarchical Energy-Efficient Method Based on Combinatorial Optimization for Wireless Sensor Networks.

    Science.gov (United States)

    Chang, Yuchao; Tang, Hongying; Cheng, Yongbo; Zhao, Qin; Yuan, Baoqing Li andXiaobing

    2017-07-19

    Routing protocols based on topology control are significantly important for improving network longevity in wireless sensor networks (WSNs). Traditionally, some WSN routing protocols distribute uneven network traffic load to sensor nodes, which is not optimal for improving network longevity. Differently to conventional WSN routing protocols, we propose a dynamic hierarchical protocol based on combinatorial optimization (DHCO) to balance energy consumption of sensor nodes and to improve WSN longevity. For each sensor node, the DHCO algorithm obtains the optimal route by establishing a feasible routing set instead of selecting the cluster head or the next hop node. The process of obtaining the optimal route can be formulated as a combinatorial optimization problem. Specifically, the DHCO algorithm is carried out by the following procedures. It employs a hierarchy-based connection mechanism to construct a hierarchical network structure in which each sensor node is assigned to a special hierarchical subset; it utilizes the combinatorial optimization theory to establish the feasible routing set for each sensor node, and takes advantage of the maximum-minimum criterion to obtain their optimal routes to the base station. Various results of simulation experiments show effectiveness and superiority of the DHCO algorithm in comparison with state-of-the-art WSN routing algorithms, including low-energy adaptive clustering hierarchy (LEACH), hybrid energy-efficient distributed clustering (HEED), genetic protocol-based self-organizing network clustering (GASONeC), and double cost function-based routing (DCFR) algorithms.

  11. Hierarchical mutual information for the comparison of hierarchical community structures in complex networks

    CERN Document Server

    Perotti, Juan Ignacio; Caldarelli, Guido

    2015-01-01

    The quest for a quantitative characterization of community and modular structure of complex networks produced a variety of methods and algorithms to classify different networks. However, it is not clear if such methods provide consistent, robust and meaningful results when considering hierarchies as a whole. Part of the problem is the lack of a similarity measure for the comparison of hierarchical community structures. In this work we give a contribution by introducing the {\\it hierarchical mutual information}, which is a generalization of the traditional mutual information, and allows to compare hierarchical partitions and hierarchical community structures. The {\\it normalized} version of the hierarchical mutual information should behave analogously to the traditional normalized mutual information. Here, the correct behavior of the hierarchical mutual information is corroborated on an extensive battery of numerical experiments. The experiments are performed on artificial hierarchies, and on the hierarchical ...

  12. Cluster Chemistry

    Institute of Scientific and Technical Information of China (English)

    2011-01-01

    @@ Cansisting of eight scientists from the State Key Laboratory of Physical Chemistry of Solid Surfaces and Xiamen University, this creative research group is devoted to the research of cluster chemistry and creation of nanomaterials.After three-year hard work, the group scored a series of encouraging progresses in synthesis of clusters with special structures, including novel fullerenes, fullerene-like metal cluster compounds as well as other related nanomaterials, and their properties study.

  13. Hierarchical Segmentation of Falsely Touching Characters from Camera Captured Degraded Document Images

    Directory of Open Access Journals (Sweden)

    Satadal Saha

    2011-07-01

    Full Text Available An innovative hierarchical image segmentation scheme is reported in this research communication. Unlike static/ spatially divided sub-images, the current innovation concentrates on object level hierarchy for segmentation of gray scale or color images into constituent component/ sub-parts. As for example, a gray scale document image may be segmented (binarized in case of two-level segmentation into connected foreground components (text/ graphics and background component by hierarchically applying a gray level threshold selection algorithm in the object-space. In any hierarchy, constituent objects are identified as connected foreground pixels, as classified by the gray scale threshold selection algorithm. To preserve the global information, thresholds for each object in any hierarchy are estimated as a weighted aggregate of the current and previous thresholds relevant to the object. The developed technique may be customized as a general purpose hierarchical information clustering algorithm in the domain of pattern analysis, data mining, bioinformatics etc.

  14. Bayesian Mass Estimates of the Milky Way: Including measurement uncertainties with hierarchical Bayes

    CERN Document Server

    Eadie, Gwendolyn; Harris, William

    2016-01-01

    We present a hierarchical Bayesian method for estimating the total mass and mass profile of the Milky Way Galaxy. The new hierarchical Bayesian approach further improves the framework presented by Eadie, Harris, & Widrow (2015) and Eadie & Harris (2016) and builds upon the preliminary reports by Eadie et al (2015a,c). The method uses a distribution function $f(\\mathcal{E},L)$ to model the galaxy and kinematic data from satellite objects such as globular clusters to trace the Galaxy's gravitational potential. A major advantage of the method is that it not only includes complete and incomplete data simultaneously in the analysis, but also incorporates measurement uncertainties in a coherent and meaningful way. We first test the hierarchical Bayesian framework, which includes measurement uncertainties, using the same data and power-law model assumed in Eadie & Harris (2016), and find the results are similar but more strongly constrained. Next, we take advantage of the new statistical framework and in...

  15. Joint Hierarchical Category Structure Learning and Large-Scale Image Classification

    Science.gov (United States)

    Qu, Yanyun; Lin, Li; Shen, Fumin; Lu, Chang; Wu, Yang; Xie, Yuan; Tao, Dacheng

    2017-09-01

    We investigate the scalable image classification problem with a large number of categories. Hierarchical visual data structures are helpful for improving the efficiency and performance of large-scale multi-class classification. We propose a novel image classification method based on learning hierarchical inter-class structures. Specifically, we first design a fast algorithm to compute the similarity metric between categories, based on which a visual tree is constructed by hierarchical spectral clustering. Using the learned visual tree, a test sample label is efficiently predicted by searching for the best path over the entire tree. The proposed method is extensively evaluated on the ILSVRC2010 and Caltech 256 benchmark datasets. Experimental results show that our method obtains significantly better category hierarchies than other state-of-the-art visual tree-based methods and, therefore, much more accurate classification.

  16. Multilevel compression of random walks on networks reveals hierarchical organization in large integrated systems

    CERN Document Server

    Rosvall, M

    2010-01-01

    To comprehend the hierarchical organization of large integrated systems, we introduce the hierarchical map equation that reveals multilevel structures in networks. In this information-theoretic approach, we exploit the duality between compression and pattern detection; by compressing a description of a random walker as a proxy for real flow on a network, we find regularities in the network that induce this system-wide flow. Finding the shortest multilevel description of the random walker therefore gives us the best hierarchical clustering of the network, the optimal number of levels and modular partition at each level, with respect to the dynamics on the network. With a novel search algorithm, we extract and illustrate the rich multilevel organization of several large social and biological networks. For example, from the global air traffic network we uncover countries and continents, and from the pattern of scientific communication we reveal more than 100 scientific fields organized in four major disciplines:...

  17. BioCluster:Tool for Identification and Clustering of Enterobacteriaceae Based on Biochemical Data

    Institute of Scientific and Technical Information of China (English)

    Ahmed Abdullah; S.M.Sabbir Alam; Munawar Sultana; M.Anwar Hossain

    2015-01-01

    Presumptive identification of different Enterobacteriaceae species is routinely achieved based on biochemical properties. Traditional practice includes manual comparison of each biochem-ical property of the unknown sample with known reference samples and inference of its identity based on the maximum similarity pattern with the known samples. This process is labor-intensive, time-consuming, error-prone, and subjective. Therefore, automation of sorting and sim-ilarity in calculation would be advantageous. Here we present a MATLAB-based graphical user interface (GUI) tool named BioCluster. This tool was designed for automated clustering and iden-tification of Enterobacteriaceae based on biochemical test results. In this tool, we used two types of algorithms, i.e., traditional hierarchical clustering (HC) and the Improved Hierarchical Clustering (IHC), a modified algorithm that was developed specifically for the clustering and identification of Enterobacteriaceae species. IHC takes into account the variability in result of 1–47 biochemical tests within this Enterobacteriaceae family. This tool also provides different options to optimize the clus-tering in a user-friendly way. Using computer-generated synthetic data and some real data, we have demonstrated that BioCluster has high accuracy in clustering and identifying enterobacterial species based on biochemical test data. This tool can be freely downloaded at http://microbialgen.du.ac.bd/biocluster/.

  18. Tracing cosmic evolution with clusters of galaxies

    CERN Document Server

    Voit, G M

    2004-01-01

    The most successful cosmological models to date envision structure formation as a hierarchical process in which gravity is constantly drawing lumps of matter together to form increasingly larger structures. Clusters of galaxies currently sit atop this hierarchy as the largest objects that have had time to collapse under the influence of their own gravity. Thus, their appearance on the cosmic scene is also relatively recent. Two features of clusters make them uniquely useful tracers of cosmic evolution. First, clusters are the biggest things whose masses we can reliably measure because they are the largest objects to have undergone gravitational relaxation and entered into virial equilibrium. Mass measurements of nearby clusters can therefore be used to determine the amount of structure in the universe on scales of 10^14 to 10^15 solar masses, and comparisons of the present-day cluster mass distribution with the mass distribution at earlier times can be used to measure the rate of structure formation, placing ...

  19. Worldwide clustering of the corruption perception

    Science.gov (United States)

    Paulus, Michal; Kristoufek, Ladislav

    2015-06-01

    We inspect a possible clustering structure of the corruption perception among 134 countries. Using the average linkage clustering, we uncover a well-defined hierarchy in the relationships among countries. Four main clusters are identified and they suggest that countries worldwide can be quite well separated according to their perception of corruption. Moreover, we find a strong connection between corruption levels and a stage of development inside the clusters. The ranking of countries according to their corruption perfectly copies the ranking according to the economic performance measured by the gross domestic product per capita of the member states. To the best of our knowledge, this study is the first one to present an application of hierarchical and clustering methods to the specific case of corruption.

  20. Cosmic Clustering

    CERN Document Server

    Anninos, Dionysios

    2011-01-01

    We show that the late time Hartle-Hawking wave function for a free massless scalar in a fixed de Sitter background encodes a sharp ultrametric structure for the standard Euclidean distance on the space of field configurations. This implies a hierarchical, tree-like organization of the state space, reflecting its genesis as a branched diffusion process. An equivalent mathematical structure organizes the state space of the Sherrington-Kirkpatrick model of a spin glass.

  1. Cosmic clustering

    Science.gov (United States)

    Anninos, Dionysios; Denef, Frederik

    2016-06-01

    We show that the late time Hartle-Hawking wave function for a free massless scalar in a fixed de Sitter background encodes a sharp ultrametric structure for the standard Euclidean distance on the space of field configurations. This implies a hierarchical, tree-like organization of the state space, reflecting its genesis as a branched diffusion process. An equivalent mathematical structure organizes the state space of the Sherrington-Kirkpatrick model of a spin glass.

  2. Clustered regression with unknown clusters

    CERN Document Server

    Barman, Kishor

    2011-01-01

    We consider a collection of prediction experiments, which are clustered in the sense that groups of experiments ex- hibit similar relationship between the predictor and response variables. The experiment clusters as well as the regres- sion relationships are unknown. The regression relation- ships define the experiment clusters, and in general, the predictor and response variables may not exhibit any clus- tering. We call this prediction problem clustered regres- sion with unknown clusters (CRUC) and in this paper we focus on linear regression. We study and compare several methods for CRUC, demonstrate their applicability to the Yahoo Learning-to-rank Challenge (YLRC) dataset, and in- vestigate an associated mathematical model. CRUC is at the crossroads of many prior works and we study several prediction algorithms with diverse origins: an adaptation of the expectation-maximization algorithm, an approach in- spired by K-means clustering, the singular value threshold- ing approach to matrix rank minimization u...

  3. Hierarchical Image Segmentation of Remotely Sensed Data using Massively Parallel GNU-LINUX Software

    Science.gov (United States)

    Tilton, James C.

    2003-01-01

    A hierarchical set of image segmentations is a set of several image segmentations of the same image at different levels of detail in which the segmentations at coarser levels of detail can be produced from simple merges of regions at finer levels of detail. In [1], Tilton, et a1 describes an approach for producing hierarchical segmentations (called HSEG) and gave a progress report on exploiting these hierarchical segmentations for image information mining. 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 (HSWO) approach to region growing, which was described as early as 1989 by Beaulieu and Goldberg. The HSWO approach seeks to produce segmentations that are more optimized than those produced by more classic approaches to region growing (e.g. Horowitz and T. Pavlidis, [3]). 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 utility of 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) was devised, which includes special code to avoid processing artifacts caused by RHSEG s recursive subdivision of the image data. The recursive nature of RHSEG makes for a straightforward parallel implementation. This paper describes the HSEG algorithm, its recursive formulation (referred to as RHSEG), and the implementation of RHSEG using massively parallel GNU-LINUX software. Results with Landsat TM data are included comparing RHSEG with classic

  4. Interactive visual exploration and refinement of cluster assignments.

    Science.gov (United States)

    Kern, Michael; Lex, Alexander; Gehlenborg, Nils; Johnson, Chris R

    2017-09-12

    With ever-increasing amounts of data produced in biology research, scientists are in need of efficient data analysis methods. Cluster analysis, combined with visualization of the results, is one such method that can be used to make sense of large data volumes. At the same time, cluster analysis is known to be imperfect and depends on the choice of algorithms, parameters, and distance measures. Most clustering algorithms don't properly account for ambiguity in the source data, as records are often assigned to discrete clusters, even if an assignment is unclear. While there are metrics and visualization techniques that allow analysts to compare clusterings or to judge cluster quality, there is no comprehensive method that allows analysts to evaluate, compare, and refine cluster assignments based on the source data, derived scores, and contextual data. In this paper, we introduce a method that explicitly visualizes the quality of cluster assignments, allows comparisons of clustering results and enables analysts to manually curate and refine cluster assignments. Our methods are applicable to matrix data clustered with partitional, hierarchical, and fuzzy clustering algorithms. Furthermore, we enable analysts to explore clustering results in context of other data, for example, to observe whether a clustering of genomic data results in a meaningful differentiation in phenotypes. Our methods are integrated into Caleydo StratomeX, a popular, web-based, disease subtype analysis tool. We show in a usage scenario that our approach can reveal ambiguities in cluster assignments and produce improved clusterings that better differentiate genotypes and phenotypes.

  5. Subspace clustering through attribute clustering

    Institute of Scientific and Technical Information of China (English)

    Kun NIU; Shubo ZHANG; Junliang CHEN

    2008-01-01

    Many recently proposed subspace clustering methods suffer from two severe problems. First, the algorithms typically scale exponentially with the data dimensionality or the subspace dimensionality of clusters. Second, the clustering results are often sensitive to input parameters. In this paper, a fast algorithm of subspace clustering using attribute clustering is proposed to over-come these limitations. This algorithm first filters out redundant attributes by computing the Gini coefficient. To evaluate the correlation of every two non-redundant attributes, the relation matrix of non-redundant attributes is constructed based on the relation function of two dimensional united Gini coefficients. After applying an overlapping clustering algorithm on the relation matrix, the candidate of all interesting subspaces is achieved. Finally, all subspace clusters can be derived by clustering on interesting subspaces. Experiments on both synthesis and real datasets show that the new algorithm not only achieves a significant gain of runtime and quality to find subspace clusters, but also is insensitive to input parameters.

  6. A spatial analysis of hierarchical waste transport structures under growing demand.

    Science.gov (United States)

    Tanguy, Audrey; Glaus, Mathias; Laforest, Valérie; Villot, Jonathan; Hausler, Robert

    2016-10-01

    The design of waste management systems rarely accounts for the spatio-temporal evolution of the demand. However, recent studies suggest that this evolution affects the planning of waste management activities like the choice and location of treatment facilities. As a result, the transport structure could also be affected by these changes. The objective of this paper is to study the influence of the spatio-temporal evolution of the demand on the strategic planning of a waste transport structure. More particularly this study aims at evaluating the effect of varying spatial parameters on the economic performance of hierarchical structures (with one transfer station). To this end, three consecutive generations of three different spatial distributions were tested for hierarchical and non-hierarchical transport structures based on costs minimization. Results showed that a hierarchical structure is economically viable for large and clustered spatial distributions. The distance parameter was decisive but the loading ratio of trucks and the formation of clusters of sources also impacted the attractiveness of the transfer station. Thus the territories' morphology should influence strategies as regards to the installation of transfer stations. The use of spatial-explicit tools such as the transport model presented in this work that take into account the territory's evolution are needed to help waste managers in the strategic planning of waste transport structures.

  7. Hierarchically structured, nitrogen-doped carbon membranes

    KAUST Repository

    Wang, Hong

    2017-08-03

    The present invention is a structure, method of making and method of use for a novel macroscopic hierarchically structured, nitrogen-doped, nano-porous carbon membrane (HNDCMs) with asymmetric and hierarchical pore architecture that can be produced on a large-scale approach. The unique HNDCM holds great promise as components in separation and advanced carbon devices because they could offer unconventional fluidic transport phenomena on the nanoscale. Overall, the invention set forth herein covers a hierarchically structured, nitrogen-doped carbon membranes and methods of making and using such a membranes.

  8. A Model for Slicing JAVA Programs Hierarchically

    Institute of Scientific and Technical Information of China (English)

    Bi-Xin Li; Xiao-Cong Fan; Jun Pang; Jian-Jun Zhao

    2004-01-01

    Program slicing can be effectively used to debug, test, analyze, understand and maintain objectoriented software. In this paper, a new slicing model is proposed to slice Java programs based on their inherent hierarchical feature. The main idea of hierarchical slicing is to slice programs in a stepwise way, from package level, to class level, method level, and finally up to statement level. The stepwise slicing algorithm and the related graph reachability algorithms are presented, the architecture of the Java program Analyzing Tool (JATO) based on hierarchical slicing model is provided, the applications and a small case study are also discussed.

  9. Hierarchical analysis of acceptable use policies

    Directory of Open Access Journals (Sweden)

    P. A. Laughton

    2008-01-01

    Full Text Available Acceptable use policies (AUPs are vital tools for organizations to protect themselves and their employees from misuse of computer facilities provided. A well structured, thorough AUP is essential for any organization. It is impossible for an effective AUP to deal with every clause and remain readable. For this reason, some sections of an AUP carry more weight than others, denoting importance. The methodology used to develop the hierarchical analysis is a literature review, where various sources were consulted. This hierarchical approach to AUP analysis attempts to highlight important sections and clauses dealt with in an AUP. The emphasis of the hierarchal analysis is to prioritize the objectives of an AUP.

  10. Hierarchical modeling and analysis for spatial data

    CERN Document Server

    Banerjee, Sudipto; Gelfand, Alan E

    2003-01-01

    Among the many uses of hierarchical modeling, their application to the statistical analysis of spatial and spatio-temporal data from areas such as epidemiology And environmental science has proven particularly fruitful. Yet to date, the few books that address the subject have been either too narrowly focused on specific aspects of spatial analysis, or written at a level often inaccessible to those lacking a strong background in mathematical statistics.Hierarchical Modeling and Analysis for Spatial Data is the first accessible, self-contained treatment of hierarchical methods, modeling, and dat

  11. Selections of data preprocessing methods and similarity metrics for gene cluster analysis

    Institute of Scientific and Technical Information of China (English)

    YANG Chunmei; WAN Baikun; GAO Xiaofeng

    2006-01-01

    Clustering is one of the major exploratory techniques for gene expression data analysis. Only with suitable similarity metrics and when datasets are properly preprocessed, can results of high quality be obtained in cluster analysis. In this study, gene expression datasets with external evaluation criteria were preprocessed as normalization by line, normalization by column or logarithm transformation by base-2, and were subsequently clustered by hierarchical clustering, k-means clustering and self-organizing maps (SOMs) with Pearson correlation coefficient or Euclidean distance as similarity metric. Finally, the quality of clusters was evaluated by adjusted Rand index. The results illustrate that k-means clustering and SOMs have distinct advantages over hierarchical clustering in gene clustering, and SOMs are a bit better than k-means when randomly initialized. It also shows that hierarchical clustering prefers Pearson correlation coefficient as similarity metric and dataset normalized by line. Meanwhile, k-means clustering and SOMs can produce better clusters with Euclidean distance and logarithm transformed datasets. These results will afford valuable reference to the implementation of gene expression cluster analysis.

  12. Identifying overlapping and hierarchical thematic structures in networks of scholarly papers: a comparison of three approaches.

    Science.gov (United States)

    Havemann, Frank; Gläser, Jochen; Heinz, Michael; Struck, Alexander

    2012-01-01

    The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.

  13. Identifying overlapping and hierarchical thematic structures in networks of scholarly papers: a comparison of three approaches.

    Directory of Open Access Journals (Sweden)

    Frank Havemann

    Full Text Available The aim of this paper is to introduce and assess three algorithms for the identification of overlapping thematic structures in networks of papers. We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles, abstracts, and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three predefined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.

  14. Identifying Overlapping and Hierarchical Thematic Structures in Networks of Scholarly Papers: A Comparison of Three Approaches

    CERN Document Server

    Havemann, Frank; Heinz, Michael; Struck, Alexander

    2011-01-01

    We implemented three recently proposed approaches to the identification of overlapping and hierarchical substructures in graphs and applied the corresponding algorithms to a network of 492 information-science papers coupled via their cited sources. The thematic substructures obtained and overlaps produced by the three hierarchical cluster algorithms were compared to a content-based categorisation, which we based on the interpretation of titles and keywords. We defined sets of papers dealing with three topics located on different levels of aggregation: h-index, webometrics, and bibliometrics. We identified these topics with branches in the dendrograms produced by the three cluster algorithms and compared the overlapping topics they detected with one another and with the three pre-defined paper sets. We discuss the advantages and drawbacks of applying the three approaches to paper networks in research fields.

  15. Image meshing via hierarchical optimization

    Institute of Scientific and Technical Information of China (English)

    Hao XIE; Ruo-feng TONG‡

    2016-01-01

    Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., defi nition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to fi nd a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to fi nd a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to fi ner ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.

  16. Image meshing via hierarchical optimization*

    Institute of Scientific and Technical Information of China (English)

    Hao XIE; Ruo-feng TONGS

    2016-01-01

    Vector graphic, as a kind of geometric representation of raster images, has many advantages, e.g., definition independence and editing facility. A popular way to convert raster images into vector graphics is image meshing, the aim of which is to find a mesh to represent an image as faithfully as possible. For traditional meshing algorithms, the crux of the problem resides mainly in the high non-linearity and non-smoothness of the objective, which makes it difficult to find a desirable optimal solution. To ameliorate this situation, we present a hierarchical optimization algorithm solving the problem from coarser levels to finer ones, providing initialization for each level with its coarser ascent. To further simplify the problem, the original non-convex problem is converted to a linear least squares one, and thus becomes convex, which makes the problem much easier to solve. A dictionary learning framework is used to combine geometry and topology elegantly. Then an alternating scheme is employed to solve both parts. Experiments show that our algorithm runs fast and achieves better results than existing ones for most images.

  17. Hierarchical Bayes Ensemble Kalman Filtering

    CERN Document Server

    Tsyrulnikov, Michael

    2015-01-01

    Ensemble Kalman filtering (EnKF), when applied to high-dimensional systems, suffers from an inevitably small affordable ensemble size, which results in poor estimates of the background error covariance matrix ${\\bf B}$. The common remedy is a kind of regularization, usually an ad-hoc spatial covariance localization (tapering) combined with artificial covariance inflation. Instead of using an ad-hoc regularization, we adopt the idea by Myrseth and Omre (2010) and explicitly admit that the ${\\bf B}$ matrix is unknown and random and estimate it along with the state (${\\bf x}$) in an optimal hierarchical Bayes analysis scheme. We separate forecast errors into predictability errors (i.e. forecast errors due to uncertainties in the initial data) and model errors (forecast errors due to imperfections in the forecast model) and include the two respective components ${\\bf P}$ and ${\\bf Q}$ of the ${\\bf B}$ matrix into the extended control vector $({\\bf x},{\\bf P},{\\bf Q})$. Similarly, we break the traditional backgrou...

  18. Hierarchical Web Page Classification Based on a Topic Model and Neighboring Pages Integration

    OpenAIRE

    Sriurai, Wongkot; Meesad, Phayung; Haruechaiyasak, Choochart

    2010-01-01

    Most Web page classification models typically apply the bag of words (BOW) model to represent the feature space. The original BOW representation, however, is unable to recognize semantic relationships between terms. One possible solution is to apply the topic model approach based on the Latent Dirichlet Allocation algorithm to cluster the term features into a set of latent topics. Terms assigned into the same topic are semantically related. In this paper, we propose a novel hierarchical class...

  19. NEW METHOD TO ESTIMATE SCALING OF POWER-LAW DEGREE DISTRIBUTION AND HIERARCHICAL NETWORKS

    Institute of Scientific and Technical Information of China (English)

    YANG Bo; DUAN Wen-qi; CHEN Zhong

    2006-01-01

    A new method and corresponding numerical procedure are introduced to estimate scaling exponents of power-law degree distribution and hierarchical clustering func tion for complex networks. This method can overcome the biased and inaccurate faults of graphical linear fitting methods commonly used in current network research. Furthermore, it is verified to have higher goodness-of-fit than graphical methods by comparing the KS (Kolmogorov-Smirnov) test statistics for 10 CNN (Connecting Nearest-Neighbor)networks.

  20. Hierarchical fractional-step approximations and parallel kinetic Monte Carlo algorithms

    OpenAIRE

    Arampatzis, Giorgos; Katsoulakis, Markos A.; Plechac, Petr; Taufer, Michela; Xu, Lifan

    2011-01-01

    We present a mathematical framework for constructing and analyzing parallel algorithms for lattice Kinetic Monte Carlo (KMC) simulations. The resulting algorithms have the capacity to simulate a wide range of spatio-temporal scales in spatially distributed, non-equilibrium physiochemical processes with complex chemistry and transport micro-mechanisms. The algorithms can be tailored to specific hierarchical parallel architectures such as multi-core processors or clusters of Graphical Processin...