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Sample records for 1842676957299765latent class cluster

  1. Context-sensitive intra-class clustering

    Yu, Yingwei

    2014-02-01

    This paper describes a new semi-supervised learning algorithm for intra-class clustering (ICC). ICC partitions each class into sub-classes in order to minimize overlap across clusters from different classes. This is achieved by allowing partitioning of a certain class to be assisted by data points from other classes in a context-dependent fashion. The result is that overlap across sub-classes (both within- and across class) is greatly reduced. ICC is particularly useful when combined with algorithms that assume that each class has a unimodal Gaussian distribution (e.g., Linear Discriminant Analysis (LDA), quadratic classifiers), an assumption that is not always true in many real-world situations. ICC can help partition non-Gaussian, multimodal distributions to overcome such a problem. In this sense, ICC works as a preprocessor. Experiments with our ICC algorithm on synthetic data sets and real-world data sets indicated that it can significantly improve the performance of LDA and quadratic classifiers. We expect our approach to be applicable to a broader class of pattern recognition problems where class-conditional densities are significantly non-Gaussian or multi-modal. © 2013 Elsevier Ltd. All rights reserved.

  2. Identifying Clusters of Concepts in a Low Cohesive Class for Extract Class Refactoring Using Metrics Supplemented Agglomerative Clustering Technique

    Rao, A Ananda

    2012-01-01

    Object oriented software with low cohesive classes can increase maintenance cost. Low cohesive classes are likely to be introduced into the software during initial design due to deviation from design principles and during evolution due to software deterioration. Low cohesive class performs operations that should be done by two or more classes. The low cohesive classes need to be identified and refactored using extract class refactoring to improve the cohesion. In this regard, two aspects are involved; the first one is to identify the low cohesive classes and the second one is to identify the clusters of concepts in the low cohesive classes for extract class refactoring. In this paper, we propose metrics supplemented agglomerative clustering technique for covering the above two aspects. The proposed metrics are validated using Weyuker's properties. The approach is applied successfully on two examples and on a case study.

  3. Class Restricted Clustering and Micro-Perturbation for Data Privacy

    Li, Xiao-Bai; Sarkar, Sumit

    2013-01-01

    The extensive use of information technologies by organizations to collect and share personal data has raised strong privacy concerns. To respond to the public’s demand for data privacy, a class of clustering-based data masking techniques is increasingly being used for privacy-preserving data sharing and analytics. Traditional clustering-based approaches for masking numeric attributes, while addressing re-identification risks, typically do not consider the disclosure risk of categorical confid...

  4. Arabic web pages clustering and annotation using semantic class features

    Hanan M. Alghamdi

    2014-12-01

    Full Text Available To effectively manage the great amount of data on Arabic web pages and to enable the classification of relevant information are very important research problems. Studies on sentiment text mining have been very limited in the Arabic language because they need to involve deep semantic processing. Therefore, in this paper, we aim to retrieve machine-understandable data with the help of a Web content mining technique to detect covert knowledge within these data. We propose an approach to achieve clustering with semantic similarities. This approach comprises integrating k-means document clustering with semantic feature extraction and document vectorization to group Arabic web pages according to semantic similarities and then show the semantic annotation. The document vectorization helps to transform text documents into a semantic class probability distribution or semantic class density. To reach semantic similarities, the approach extracts the semantic class features and integrates them into the similarity weighting schema. The quality of the clustering result has evaluated the use of the purity and the mean intra-cluster distance (MICD evaluation measures. We have evaluated the proposed approach on a set of common Arabic news web pages. We have acquired favorable clustering results that are effective in minimizing the MICD, expanding the purity and lowering the runtime.

  5. Filling the gap: a new class of old star cluster?

    Forbes, Duncan; Usher, Christopher; Strader, Jay; Romanowsky, Aaron; Brodie, Jean; Arnold, Jacob; Spitler, Lee

    2013-01-01

    It is not understood whether long-lived star clusters possess a continuous range of sizes and masses (and hence densities), or if rather, they should be considered as distinct types with different origins. Utilizing the Hubble Space Telescope (HST) to measure sizes, and long exposures on the Keck 10m telescope to obtain distances, we have discovered the first confirmed star clusters that lie within a previously claimed size-luminosity gap dubbed the `avoidance zone' by Hwang et al (2011). The existence of these star clusters extends the range of sizes, masses and densities for star clusters, and argues against current formation models that predict well-defined size-mass relationships (such as stripped nuclei, giant globular clusters or merged star clusters). The red colours of these gap objects suggests that they are not a new class of object but are related to Faint Fuzzies observed in nearby lenticular galaxies. We also report a number of low luminosity UCDs with sizes of up to 50 pc. Future, statistically ...

  6. ADHD latent class clusters: DSM-IV subtypes and comorbidity.

    Elia, Josephine; Arcos-Burgos, Mauricio; Bolton, Kelly L; Ambrosini, Paul J; Berrettini, Wade; Muenke, Maximilian

    2009-12-30

    ADHD (Attention Deficit Hyperactivity Disorder) has a complex, heterogeneous phenotype only partially captured by Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria. In this report, latent class analyses (LCA) are used to identify ADHD phenotypes using K-SADS-IVR (Schedule for Affective Disorders & Schizophrenia for School Age Children-IV-Revised) symptoms and symptom severity data from a clinical sample of 500 ADHD subjects, ages 6-18, participating in an ADHD genetic study. Results show that LCA identified six separate ADHD clusters, some corresponding to specific DSM-IV subtypes while others included several subtypes. DSM-IV comorbid anxiety and mood disorders were generally similar across all clusters, and subjects without comorbidity did not aggregate within any one cluster. Age and gender composition also varied. These results support findings from population-based LCA studies. The six clusters provide additional homogenous groups that can be used to define ADHD phenotypes in genetic association studies. The limited age ranges aggregating in the different clusters may prove to be a particular advantage in genetic studies where candidate gene expression may vary during developmental phases. DSM-IV comorbid mood and anxiety disorders also do not appear to increase cluster heterogeneity; however, longitudinal studies that cover period of risk are needed to support this finding. PMID:19900717

  7. Patterns of Brucellosis Infection Symptoms in Azerbaijan: A Latent Class Cluster Analysis

    Rita Ismayilova; Emilya Nasirova; Colleen Hanou; Rivard, Robert G.; Bautista, Christian T.

    2014-01-01

    Brucellosis infection is a multisystem disease, with a broad spectrum of symptoms. We investigated the existence of clusters of infected patients according to their clinical presentation. Using national surveillance data from the Electronic-Integrated Disease Surveillance System, we applied a latent class cluster (LCC) analysis on symptoms to determine clusters of brucellosis cases. A total of 454 cases reported between July 2011 and July 2013 were analyzed. LCC identified a two-cluster model...

  8. Parallel unstructured AMR and gigabit networking for Beowulf-class clusters

    Norton, C. D.; Cwik, T. A.

    2001-01-01

    The impact of gigabit networking with Myrinet 2000 hardware and MPICH-GM software on a 2-way SMP Beowulf-class cluster for parallel unstructured adaptive mesh refinement using the PYRAMID library is described.

  9. Teleportation of an Arbitrary Two-Particle State via a Single Cluster-Class State

    Teleportation of an arbitrary two-qubit state with a single partially entangled state, a four-qubit linear cluster-class state, is studied. The case is more practical than previous ones using maximally entangled states as the quantum channel. In order to realize teleportation, we first construct a cluster-basis of 16 orthonormal cluster states. We show that quantum teleportation can be successfully implemented with a certain probability if the receiver can adopt appropriate unitary transformations after receiving the sender's cluster-basis measurement information. In addition, an important conclusion can be obtained that a four-qubit maximally entangled state (cluster state) can be extracted from a single copy of the cluster-class state with the same probability as the teleportation in principle. (general)

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

    Landfors Mattias

    2010-10-01

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

  11. ADHD latent class clusters: DSM-IV subtypes and comorbidity

    Elia, Josephine; Arcos-Burgos, Mauricio; Bolton, Kelly L.; Ambrosini, Paul J.; Berrettini, Wade; Muenke, Maximilian

    2009-01-01

    ADHD (Attention Deficit Hyperactivity Disorder) has a complex, heterogeneous phenotype only partially captured by Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) criteria. In this report, latent class analyses (LCA) are used to identify ADHD phenotypes using K-SADS-IVR (Schedule for Affective Disorders & Schizophrenia for School Age Children-IV-Revised) symptoms and symptom severity data from a clinical sample of 500 ADHD subjects, ages 6–18, participating in an ADHD genetic st...

  12. Cyclist–motorist crash patterns in Denmark: A latent class clustering approach

    Kaplan, Sigal; Prato, Carlo Giacomo

    2013-01-01

    Objective: The current study aimed at uncovering patterns of cyclist–motorist crashes in Denmark and investigating their prevalence and severity. The importance of implementing clustering techniques for providing a holistic overview of vulnerable road users’ crash patterns derives from the need to...... prioritize safety issues and to devise efficient preventive measures. Method: The current study focused on cyclist–motorist crashes that occurred in Denmark during the period between 2007 and 2011. To uncover crash patterns, the current analysis applied latent class clustering, an unsupervised probabilistic...... clustering approach that relies on the statistical concept of likelihood and allows partial overlap across clusters. Results: The analysis yielded 13 distinguishable cyclist–motorist latent classes. Specific crash patterns for urban and rural areas were revealed. Prevalent features that allowed...

  13. Bond percolation on a class of correlated and clustered random graphs

    We introduce a formalism for computing bond percolation properties of a class of correlated and clustered random graphs. This class of graphs is a generalization of the configuration model where nodes of different types are connected via different types of hyperedges, edges that can link more than two nodes. We argue that the multitype approach coupled with the use of clustered hyperedges can reproduce a wide spectrum of complex patterns, and thus enhances our capability to model real complex networks. As an illustration of this claim, we use our formalism to highlight unusual behaviours of the size and composition of the components (small and giant) in a synthetic, albeit realistic, social network. (paper)

  14. Semi-Automatically Inducing Semantic Classes of Clinical Research Eligibility Criteria Using UMLS and Hierarchical Clustering

    Luo, Zhihui; Johnson, Stephen B.; Weng, Chunhua

    2010-01-01

    This paper presents a novel approach to learning semantic classes of clinical research eligibility criteria. It uses the UMLS Semantic Types to represent semantic features and the Hierarchical Clustering method to group similar eligibility criteria. By establishing a gold standard using two independent raters, we evaluated the coverage and accuracy of the induced semantic classes. On 2,718 random eligibility criteria sentences, the inter-rater classification agreement was 85.73%. In a 10-fold...

  15. The X-CLASS - redMaPPer galaxy cluster comparison: I. Identification procedures

    Sadibekova, Tatyana; Clerc, Nicolas; Faccioli, Lorenzo; Gastaud, Rene; Fevre, Jean-Paul Le; Rozo, Eduardo; Rykoff, Eli S

    2014-01-01

    We performed a detailed and, for a large part interactive, analysis of the matching output between the X-CLASS and redMaPPer cluster catalogues. The overlap between the two catalogues has been accurately determined and possible cluster positional errors were manually recovered. The final samples comprise 270 and 355 redMaPPer and X-CLASS clusters respectively. X-ray cluster matching rates were analysed as a function of optical richness. In a second step, the redMaPPer clusters were correlated with the entire X-ray catalogue, containing point and uncharacterised sources (down to a few 10^{-15} erg s^{-1} cm^{-2} in the [0.5-2] keV band). A stacking analysis was performed for the remaining undetected optical clusters. Main results show that neither of the wavebands misses any massive cluster (as coded by X-ray luminosity or optical richness). After correcting for obvious pipeline short-comings (about 10% of the cases both in optical and X-ray), ~50% of the redMaPPer (down to a richness of 20) are found to coinc...

  16. Patterns of Brucellosis Infection Symptoms in Azerbaijan: A Latent Class Cluster Analysis

    Rita Ismayilova

    2014-01-01

    Full Text Available Brucellosis infection is a multisystem disease, with a broad spectrum of symptoms. We investigated the existence of clusters of infected patients according to their clinical presentation. Using national surveillance data from the Electronic-Integrated Disease Surveillance System, we applied a latent class cluster (LCC analysis on symptoms to determine clusters of brucellosis cases. A total of 454 cases reported between July 2011 and July 2013 were analyzed. LCC identified a two-cluster model and the Vuong-Lo-Mendell-Rubin likelihood ratio supported the cluster model. Brucellosis cases in the second cluster (19% reported higher percentages of poly-lymphadenopathy, hepatomegaly, arthritis, myositis, and neuritis and changes in liver function tests compared to cases of the first cluster. Patients in the second cluster had a severe brucellosis disease course and were associated with longer delay in seeking medical attention. Moreover, most of them were from Beylagan, a region focused on sheep and goat livestock production in south-central Azerbaijan. Patients in cluster 2 accounted for one-quarter of brucellosis cases and had a more severe clinical presentation. Delay in seeking medical care may explain severe illness. Future work needs to determine the factors that influence brucellosis case seeking and identify brucellosis species, particularly among cases from Beylagan.

  17. Exploring the Relationship between Autism Spectrum Disorder and Epilepsy Using Latent Class Cluster Analysis

    Cuccaro, Michael L.; Tuchman, Roberto F.; Hamilton, Kara L.; Wright, Harry H.; Abramson, Ruth K.; Haines, Jonathan L.; Gilbert, John R.; Pericak-Vance, Margaret

    2012-01-01

    Epilepsy co-occurs frequently in autism spectrum disorders (ASD). Understanding this co-occurrence requires a better understanding of the ASD-epilepsy phenotype (or phenotypes). To address this, we conducted latent class cluster analysis (LCCA) on an ASD dataset (N = 577) which included 64 individuals with epilepsy. We identified a 5-cluster…

  18. Semi-Automatically Inducing Semantic Classes of Clinical Research Eligibility Criteria Using UMLS and Hierarchical Clustering.

    Luo, Zhihui; Johnson, Stephen B; Weng, Chunhua

    2010-01-01

    This paper presents a novel approach to learning semantic classes of clinical research eligibility criteria. It uses the UMLS Semantic Types to represent semantic features and the Hierarchical Clustering method to group similar eligibility criteria. By establishing a gold standard using two independent raters, we evaluated the coverage and accuracy of the induced semantic classes. On 2,718 random eligibility criteria sentences, the inter-rater classification agreement was 85.73%. In a 10-fold validation test, the average Precision, Recall and F-score of the classification results of a decision-tree classifier were 87.8%, 88.0%, and 87.7% respectively. Our induced classes well aligned with 16 out of 17 eligibility criteria classes defined by the BRIDGE model. We discuss the potential of this method and our future work. PMID:21347026

  19. Partnership effects in general practice: identification of clustering using intra-class correlation coefficients.

    Ashworth, Mark; Armstrong, David

    2003-01-01

    Although most United Kingdom general practitioners (GPs) work together in a shared professional arrangement termed 'partnership', little is known about the nature of such partnerships. We report the results of a survey of 61 general practice partners in 15 group practices and their attitudes to prescribing and managerial issues related to participation in a commissioning group. Intra-class correlation coefficients (ICCs) were used to explore how these individually held attitudes clustered wit...

  20. Global Clustering Quality Coefficient Assessing the Efficiency of PCA Class Assignment

    Mirela Praisler

    2014-01-01

    Full Text Available An essential factor influencing the efficiency of the predictive models built with principal component analysis (PCA is the quality of the data clustering revealed by the score plots. The sensitivity and selectivity of the class assignment are strongly influenced by the relative position of the clusters and by their dispersion. We are proposing a set of indicators inspired from analytical geometry that may be used for an objective quantitative assessment of the data clustering quality as well as a global clustering quality coefficient (GCQC that is a measure of the overall predictive power of the PCA models. The use of these indicators for evaluating the efficiency of the PCA class assignment is illustrated by a comparative study performed for the identification of the preprocessing function that is generating the most efficient PCA system screening for amphetamines based on their GC-FTIR spectra. The GCQC ranking of the tested feature weights is explained based on estimated density distributions and validated by using quadratic discriminant analysis (QDA.

  1. A class of spherical, truncated, anisotropic models for application to globular clusters

    de Vita, Ruggero; Bertin, Giuseppe; Zocchi, Alice

    2016-05-01

    Recently, a class of non-truncated, radially anisotropic models (the so-called f(ν)-models), originally constructed in the context of violent relaxation and modelling of elliptical galaxies, has been found to possess interesting qualities in relation to observed and simulated globular clusters. In view of new applications to globular clusters, we improve this class of models along two directions. To make them more suitable for the description of small stellar systems hosted by galaxies, we introduce a "tidal" truncation by means of a procedure that guarantees full continuity of the distribution function. The new fT(ν)-models are shown to provide a better fit to the observed photometric and spectroscopic profiles for a sample of 13 globular clusters studied earlier by means of non-truncated models; interestingly, the best-fit models also perform better with respect to the radial-orbit instability. Then, we design a flexible but simple two-component family of truncated models to study the separate issues of mass segregation and multiple populations. We do not aim at a fully realistic description of globular clusters to compete with the description currently obtained by means of dedicated simulations. The goal here is to try to identify the simplest models, that is, those with the smallest number of free parameters, but still have the capacity to provide a reasonable description for clusters that are evidently beyond the reach of one-component models. With this tool, we aim at identifying the key factors that characterize mass segregation or the presence of multiple populations. To reduce the relevant parameter space, we formulate a few physical arguments based on recent observations and simulations. A first application to two well-studied globular clusters is briefly described and discussed.

  2. Cluster analysis of structural stage classes to map wildland fuels in a Madrean ecosystem.

    Miller, Jay D; Danzer, Shelley R; Watts, Joseph M; Stone, Sheridan; Yool, Stephen R

    2003-07-01

    Geospatial information technology is changing the nature of fire mapping science and management. Geographic information systems (GIS) and global positioning system technology coupled with remotely sensed data provide powerful tools for mapping, assessing, and understanding the complex spatial phenomena of wildland fuels and fire hazard. The effectiveness of these technologies for fire management still depends on good baseline fuels data since techniques have yet to be developed to directly interrogate understory fuels with remotely sensed data. We couple field data collections with GIS, remote sensing, and hierarchical clustering to characterize and map the variability of wildland fuels within and across vegetation types. One hundred fifty six fuel plots were sampled in eight vegetation types ranging in elevation from 1150 to 2600 m surrounding a Madrean 'sky island' mountain range in the southwestern US. Fuel plots within individual vegetation types were divided into classes representing various stages of structural development with unique fuel load characteristics using a hierarchical clustering method. Two Landsat satellite images were then classified into vegetation/fuel classes using a hybrid unsupervised/supervised approach. A back-classification accuracy assessment, which uses the same pixels to test as used to train the classifier, produced an overall Kappa of 50% for the vegetation/fuels map. The map with fuel classes within vegetation type collapsed into single classes was verified with an independent dataset, yielding an overall Kappa of 80%. PMID:12837253

  3. NGC 6273: Towards Defining A New Class of Galactic Globular Clusters?

    Johnson, Christian I.; Rich, Robert Michael; Pilachowski, Catherine A.; Caldwell, Nelson; Mateo, Mario L.; Ira Bailey, John; Crane, Jeffrey D.

    2016-01-01

    A growing number of observations have found that several Galactic globular clusters exhibit abundance dispersions beyond the well-known light element (anti-)correlations. These clusters tend to be very massive, have >0.1 dex intrinsic metallicity dispersions, have complex sub-giant branch morphologies, and have correlated [Fe/H] and s-process element enhancements. Interestingly, nearly all of these clusters discovered so far have [Fe/H]~-1.7. In this context, we have examined the chemical composition of 18 red giant branch (RGB) stars in the massive, metal-poor Galactic bulge globular cluster NGC 6273 using high signal-to-noise, high resolution (R~27,000) spectra obtained with the Michigan/Magellan Fiber System (M2FS) and MSpec spectrograph mounted on the Magellan-Clay 6.5m telescope at Las Campanas Observatory. We find that the cluster exhibits a metallicity range from [Fe/H]=-1.80 to -1.30 and is composed of two dominant populations separated in [Fe/H] and [La/Fe] abundance. The increase in [La/Eu] as a function of [La/H] suggests that the increase in [La/Fe] with [Fe/H] is due to almost pure s-process enrichment. The most metal-rich star in our sample is not strongly La-enhanced, but is α-poor and may belong to a third "anomalous" stellar population. The two dominant populations exhibit the same [Na/Fe]-[Al/Fe] correlation found in other "normal" globular clusters. Therefore, NGC 6273 joins ω Centauri, M 22, M 2, and NGC 5286 as a possible new class of Galactic globular clusters.

  4. Incremental multi-class semi-supervised clustering regularized by Kalman filtering.

    Mehrkanoon, Siamak; Agudelo, Oscar Mauricio; Suykens, Johan A K

    2015-11-01

    This paper introduces an on-line semi-supervised learning algorithm formulated as a regularized kernel spectral clustering (KSC) approach. We consider the case where new data arrive sequentially but only a small fraction of it is labeled. The available labeled data act as prototypes and help to improve the performance of the algorithm to estimate the labels of the unlabeled data points. We adopt a recently proposed multi-class semi-supervised KSC based algorithm (MSS-KSC) and make it applicable for on-line data clustering. Given a few user-labeled data points the initial model is learned and then the class membership of the remaining data points in the current and subsequent time instants are estimated and propagated in an on-line fashion. The update of the memberships is carried out mainly using the out-of-sample extension property of the model. Initially the algorithm is tested on computer-generated data sets, then we show that video segmentation can be cast as a semi-supervised learning problem. Furthermore we show how the tracking capabilities of the Kalman filter can be used to provide the labels of objects in motion and thus regularizing the solution obtained by the MSS-KSC algorithm. In the experiments, we demonstrate the performance of the proposed method on synthetic data sets and real-life videos where the clusters evolve in a smooth fashion over time. PMID:26319050

  5. Inferring noncoding RNA families and classes by means of genome-scale structure-based clustering.

    Sebastian Will

    2007-04-01

    Full Text Available The RFAM database defines families of ncRNAs by means of sequence similarities that are sufficient to establish homology. In some cases, such as microRNAs and box H/ACA snoRNAs, functional commonalities define classes of RNAs that are characterized by structural similarities, and typically consist of multiple RNA families. Recent advances in high-throughput transcriptomics and comparative genomics have produced very large sets of putative noncoding RNAs and regulatory RNA signals. For many of them, evidence for stabilizing selection acting on their secondary structures has been derived, and at least approximate models of their structures have been computed. The overwhelming majority of these hypothetical RNAs cannot be assigned to established families or classes. We present here a structure-based clustering approach that is capable of extracting putative RNA classes from genome-wide surveys for structured RNAs. The LocARNA (local alignment of RNA tool implements a novel variant of the Sankoff algorithm that is sufficiently fast to deal with several thousand candidate sequences. The method is also robust against false positive predictions, i.e., a contamination of the input data with unstructured or nonconserved sequences. We have successfully tested the LocARNA-based clustering approach on the sequences of the RFAM-seed alignments. Furthermore, we have applied it to a previously published set of 3,332 predicted structured elements in the Ciona intestinalis genome (Missal K, Rose D, Stadler PF (2005 Noncoding RNAs in Ciona intestinalis. Bioinformatics 21 (Supplement 2: i77-i78. In addition to recovering, e.g., tRNAs as a structure-based class, the method identifies several RNA families, including microRNA and snoRNA candidates, and suggests several novel classes of ncRNAs for which to date no representative has been experimentally characterized.

  6. A conserved cluster of three PRD-class homeobox genes (homeobrain, rx and orthopedia in the Cnidaria and Protostomia

    Mazza Maureen E

    2010-07-01

    Full Text Available Abstract Background Homeobox genes are a superclass of transcription factors with diverse developmental regulatory functions, which are found in plants, fungi and animals. In animals, several Antennapedia (ANTP-class homeobox genes reside in extremely ancient gene clusters (for example, the Hox, ParaHox, and NKL clusters and the evolution of these clusters has been implicated in the morphological diversification of animal bodyplans. By contrast, similarly ancient gene clusters have not been reported among the other classes of homeobox genes (that is, the LIM, POU, PRD and SIX classes. Results Using a combination of in silico queries and phylogenetic analyses, we found that a cluster of three PRD-class homeobox genes (Homeobrain (hbn, Rax (rx and Orthopedia (otp is present in cnidarians, insects and mollusks (a partial cluster comprising hbn and rx is present in the placozoan Trichoplax adhaerens. We failed to identify this 'HRO' cluster in deuterostomes; in fact, the Homeobrain gene appears to be missing from the chordate genomes we examined, although it is present in hemichordates and echinoderms. To illuminate the ancestral organization and function of this ancient cluster, we mapped the constituent genes against the assembled genome of a model cnidarian, the sea anemone Nematostella vectensis, and characterized their spatiotemporal expression using in situ hybridization. In N. vectensis, these genes reside in a span of 33 kb with the same gene order as previously reported in insects. Comparisons of genomic sequences and expressed sequence tags revealed the presence of alternative transcripts of Nv-otp and two highly unusual protein-coding polymorphisms in the terminal helix of the Nv-rx homeodomain. A population genetic survey revealed the Rx polymorphisms to be widespread in natural populations. During larval development, all three genes are expressed in the ectoderm, in non-overlapping territories along the oral-aboral axis, with distinct

  7. Cluster exponential synchronization of a class of complex networks with hybrid coupling and time-varying delay

    This paper deals with the cluster exponential synchronization of a class of complex networks with hybrid coupling and time-varying delay. Through constructing an appropriate Lyapunov—Krasovskii functional and applying the theory of the Kronecker product of matrices and the linear matrix inequality (LMI) technique, several novel sufficient conditions for cluster exponential synchronization are obtained. These cluster exponential synchronization conditions adopt the bounds of both time delay and its derivative, which are less conservative. Finally, the numerical simulations are performed to show the effectiveness of the theoretical results. (general)

  8. Impacts of fast food and food retail environment on overweight and obesity in China: a multilevel latent class cluster approach

    Zhang XiaoYong, Xiaoyong; Lans, van der I.A.; Dagevos, H.

    2012-01-01

    Objective To simultaneously identify consumer segments based on individual-level consumption and community-level food retail environment data and to investigate whether the segments are associated with BMI and dietary knowledge in China. Design A multilevel latent class cluster model was applied to

  9. Identifying victims of workplace bullying by integrating traditional estimation approaches into a latent class cluster model.

    Leon-Perez, Jose M; Notelaers, Guy; Arenas, Alicia; Munduate, Lourdes; Medina, Francisco J

    2014-05-01

    Research findings underline the negative effects of exposure to bullying behaviors and document the detrimental health effects of being a victim of workplace bullying. While no one disputes its negative consequences, debate continues about the magnitude of this phenomenon since very different prevalence rates of workplace bullying have been reported. Methodological aspects may explain these findings. Our contribution to this debate integrates behavioral and self-labeling estimation methods of workplace bullying into a measurement model that constitutes a bullying typology. Results in the present sample (n = 1,619) revealed that six different groups can be distinguished according to the nature and intensity of reported bullying behaviors. These clusters portray different paths for the workplace bullying process, where negative work-related and person-degrading behaviors are strongly intertwined. The analysis of the external validity showed that integrating previous estimation methods into a single measurement latent class model provides a reliable estimation method of workplace bullying, which may overcome previous flaws. PMID:24257593

  10. Human HLA class I- and HLA class II-restricted cloned cytotoxic T lymphocytes identify a cluster of epitopes on the measles virus fusion protein.

    van Binnendijk, R S; Versteeg-van Oosten, J P; Poelen, M C; Brugghe, H F; Hoogerhout, P; Osterhaus, A D; Uytdehaag, F G

    1993-01-01

    The transmembrane fusion (F) glycoprotein of measles virus is an important target antigen of human HLA class I- and class II-restricted cytotoxic T lymphocytes (CTL). Genetically engineered F proteins and nested sets of synthetic peptides spanning the F protein were used to determine sequences of F recognized by a number of F-specific CTL clones. Combined N- and C-terminal deletions of the respective peptides revealed that human HLA class I and HLA class II-restricted CTL efficiently recognize nonapeptides or decapeptides representing epitopes of F. Three distinct sequences recognized by three different HLA class II (DQw1, DR2, and DR4/w53)-restricted CTL clones appear to cluster between amino acids 379 and 466 of F, thus defining an important T-cell epitope area of F. Within this same region, a nonamer peptide of F was found to be recognized by an HLA-B27-restricted CTL clone, as expected on the basis of the structural homology between this peptide and other known HLA-B27 binding peptides. PMID:7680390

  11. Merged consensus clustering to assess and improve class discovery with microarray data

    Jarman Andrew P

    2010-12-01

    Full Text Available Abstract Background One of the most commonly performed tasks when analysing high throughput gene expression data is to use clustering methods to classify the data into groups. There are a large number of methods available to perform clustering, but it is often unclear which method is best suited to the data and how to quantify the quality of the classifications produced. Results Here we describe an R package containing methods to analyse the consistency of clustering results from any number of different clustering methods using resampling statistics. These methods allow the identification of the the best supported clusters and additionally rank cluster members by their fidelity within the cluster. These metrics allow us to compare the performance of different clustering algorithms under different experimental conditions and to select those that produce the most reliable clustering structures. We show the application of this method to simulated data, canonical gene expression experiments and our own novel analysis of genes involved in the specification of the peripheral nervous system in the fruitfly, Drosophila melanogaster. Conclusions Our package enables users to apply the merged consensus clustering methodology conveniently within the R programming environment, providing both analysis and graphical display functions for exploring clustering approaches. It extends the basic principle of consensus clustering by allowing the merging of results between different methods to provide an averaged clustering robustness. We show that this extension is useful in correcting for the tendency of clustering algorithms to treat outliers differently within datasets. The R package, clusterCons, is freely available at CRAN and sourceforge under the GNU public licence.

  12. The XMM-LSS survey: optical assessment and properties of different X-ray selected cluster classes

    Adami, C; Pierre, M; Sprimont, P G; Libbrecht, C; Pacaud, F; Clerc, N; Sadibekova, T; Surdej, J; Altieri, B; Duc, P A; Galaz, G; Gueguen, A; Guennou, L; Hertling, G; Ilbert, O; LeFèvre, J P; Quintana, H; Valtchanov, I; Willis, J P; Akiyama, M; Aussel, H; Chiappetti, L; Detal, A; Garilli, B; LeBrun, V; LeFèvre, O; Maccagni, D; Melin, J B; Ponman, T J; Ricci, D; Tresse, L

    2010-01-01

    XMM and Chandra opened a new area for the study of clusters of galaxies. Not only for cluster physics but also, for the detection of faint and distant clusters that were inaccessible with previous missions. This article presents 66 spectroscopically confirmed clusters (0.05classes, of extended-sources are defined in a two-dimensional X-ray parameter space allowing for various degrees of completeness and contamination. We describe the procedure developed to assess the reality of these cluster candidates using the CFHTLS photometric data and spectroscopic information from our own follow-up campaigns. Most of these objects are low mass clusters, hence constituting a still poorly studied population. In a second step, we quantify correlations between the optical prop...

  13. Classification of Two Class Motor Imagery Tasks Using Hybrid GA-PSO Based K-Means Clustering

    Suraj; Purnendu Tiwari; Subhojit Ghosh; Rakesh Kumar Sinha

    2015-01-01

    Transferring the brain computer interface (BCI) from laboratory condition to meet the real world application needs BCI to be applied asynchronously without any time constraint. High level of dynamism in the electroencephalogram (EEG) signal reasons us to look toward evolutionary algorithm (EA). Motivated by these two facts, in this work a hybrid GA-PSO based K-means clustering technique has been used to distinguish two class motor imagery (MI) tasks. The proposed hybrid GA-PSO based K-means c...

  14. Impacts of fast food and food retail environment on overweight and obesity in China: a multilevel latent class cluster approach

    Zhang XiaoYong, Xiaoyong; Lans, van der, A.M.; Dagevos, H.

    2012-01-01

    Objective To simultaneously identify consumer segments based on individual-level consumption and community-level food retail environment data and to investigate whether the segments are associated with BMI and dietary knowledge in China. Design A multilevel latent class cluster model was applied to identify consumer segments based not only on their individual preferences for fast food, salty snack foods, and soft drinks and sugared fruit drinks, but also on the food retail environment at the ...

  15. Signaling at the inhibitory natural killer cell immune synapse regulates lipid raft polarization but not class I MHC clustering.

    Fassett, M S; Davis, D M; Valter, M M; Cohen, G B; Strominger, J L

    2001-12-01

    Natural killer (NK) cell cytotoxicity is determined by a balance of positive and negative signals. Negative signals are transmitted by NK inhibitory receptors (killer immunoglobulin-like receptors, KIR) at the site of membrane apposition between an NK cell and a target cell, where inhibitory receptors become clustered with class I MHC ligands in an organized structure known as an inhibitory NK immune synapse. Immune synapse formation in NK cells is poorly understood. Because signaling by NK inhibitory receptors could be involved in this process, the human NK tumor line YTS was transfected with signal-competent and signal-incompetent KIR2DL1. The latter were generated by truncating the KIR2DL1 cytoplasmic tail or by introducing mutations in the immunoreceptor tyrosine-based inhibition motifs. The KIR2DL1 mutants retained their ability to cluster class I MHC ligands on NK cell interaction with appropriate target cells. Therefore, receptor-ligand clustering at the inhibitory NK immune synapse occurs independently of KIR2DL1 signal transduction. However, parallel examination of NK cell membrane lipid rafts revealed that KIR2DL1 signaling is critical for blocking lipid raft polarization and NK cell cytotoxicity. Moreover, raft polarization was inhibited by reagents that disrupt microtubules and actin filaments, whereas synapse formation was not. Thus, NK lipid raft polarization and inhibitory NK immune synapse formation occur by fundamentally distinct mechanisms. PMID:11724921

  16. PARTIAL TRAINING METHOD FOR HEURISTIC ALGORITHM OF POSSIBLE CLUSTERIZATION UNDER UNKNOWN NUMBER OF CLASSES

    D. A. Viattchenin

    2009-01-01

    Full Text Available A method for constructing a subset of labeled objects which is used in a heuristic algorithm of possible  clusterization with partial  training is proposed in the  paper.  The  method  is  based  on  data preprocessing by the heuristic algorithm of possible clusterization using a transitive closure of a fuzzy tolerance. Method efficiency is demonstrated by way of an illustrative example.

  17. Manganese-centered tubular boron cluster - MnB16-: A new class of transition-metal molecules

    Jian, Tian; Li, Wan-Lu; Popov, Ivan A.; Lopez, Gary V.; Chen, Xin; Boldyrev, Alexander I.; Li, Jun; Wang, Lai-Sheng

    2016-04-01

    We report the observation of a manganese-centered tubular boron cluster (MnB16-), which is characterized by photoelectron spectroscopy and ab initio calculations. The relatively simple pattern of the photoelectron spectrum indicates the cluster to be highly symmetric. Ab initio calculations show that MnB16- has a Mn-centered tubular structure with C4v symmetry due to first-order Jahn-Teller effect, while neutral MnB16 reduces to C2v symmetry due to second-order Jahn-Teller effect. In MnB16-, two unpaired electrons are observed, one on the Mn 3dz2 orbital and another on the B16 tube, making it an unusual biradical. Strong covalent bonding is found between the Mn 3d orbitals and the B16 tube, which helps to stabilize the tubular structure. The current result suggests that there may exist a whole class of metal-stabilized tubular boron clusters. These metal-doped boron clusters provide a new bonding modality for transition metals, as well as a new avenue to design boron-based nanomaterials.

  18. CLUSTER TAXOMETRY OF ATTENTION DEFICIT/ HYPERACTIVITY DISORDER WITH LATENT CLASS AND CORRESPONDENCE ANALYSIS

    DAVID A PINEDA

    2007-08-01

    Full Text Available Attention deficit/hyperactivity disorder (ADHD has heterogeneous symptoms with diverse grades of severity. Latentclass cluster analysis (LCCA can be used to classify children, using direct data from any instrument that reports thesesymptoms, without previous gold standard diagnosis. One ADHD symptoms checklist, and one ADHD comorbiditiesquestionnaire were used. LCCAs were developed for each instrument, which were administered to a sample of 540children and adolescents, aged 4-17 years, from the regular school of Manizales-Colombia. A simple correspondenceanalysis (SCA was done to determine the relationships between the groups classified from both LCCAs. Six clusters were obtained from ADHD checklist and five from the ADHD comorbidities questionnaire. SCA found fourindependent groups, derived from the concordances between the 11 clusters obtained by the LCCAs from bothinstruments. These findings suggest that LCCA and SCA can be use as accurate taxometric procedures to classifyexternalizing psychopathologies.

  19. Clustering

    Jinfei Liu

    2013-04-01

    Full Text Available DBSCAN is a well-known density-based clustering algorithm which offers advantages for finding clusters of arbitrary shapes compared to partitioning and hierarchical clustering methods. However, there are few papers studying the DBSCAN algorithm under the privacy preserving distributed data mining model, in which the data is distributed between two or more parties, and the parties cooperate to obtain the clustering results without revealing the data at the individual parties. In this paper, we address the problem of two-party privacy preserving DBSCAN clustering. We first propose two protocols for privacy preserving DBSCAN clustering over horizontally and vertically partitioned data respectively and then extend them to arbitrarily partitioned data. We also provide performance analysis and privacy proof of our solution..

  20. CLUSTER TAXOMETRY OF ATTENTION DEFICIT/ HYPERACTIVITY DISORDER WITH LATENT CLASS AND CORRESPONDENCE ANALYSIS

    David A. Pineda; DANIEL CAMILO AGUIRRE-ACEVEDO; FRANCISCO LOPERA; DANIEL A PINEDA; MAURICIO ARCOS-BURGOS

    2007-01-01

    Attention deficit/hyperactivity disorder (ADHD) has heterogeneous symptoms with diverse grades of severity. Latentclass cluster analysis (LCCA) can be used to classify children, using direct data from any instrument that reports thesesymptoms, without previous gold standard diagnosis. One ADHD symptoms checklist, and one ADHD comorbiditiesquestionnaire were used. LCCAs were developed for each instrument, which were administered to a sample of 540children and adolescents, aged 4-17 years, from...

  1. Nonlinear dimension reduction and clustering by Minimum Curvilinearity unfold neuropathic pain and tissue embryological classes

    Cannistraci, Carlo

    2010-09-01

    Motivation: Nonlinear small datasets, which are characterized by low numbers of samples and very high numbers of measures, occur frequently in computational biology, and pose problems in their investigation. Unsupervised hybrid-two-phase (H2P) procedures-specifically dimension reduction (DR), coupled with clustering-provide valuable assistance, not only for unsupervised data classification, but also for visualization of the patterns hidden in high-dimensional feature space. Methods: \\'Minimum Curvilinearity\\' (MC) is a principle that-for small datasets-suggests the approximation of curvilinear sample distances in the feature space by pair-wise distances over their minimum spanning tree (MST), and thus avoids the introduction of any tuning parameter. MC is used to design two novel forms of nonlinear machine learning (NML): Minimum Curvilinear embedding (MCE) for DR, and Minimum Curvilinear affinity propagation (MCAP) for clustering. Results: Compared with several other unsupervised and supervised algorithms, MCE and MCAP, whether individually or combined in H2P, overcome the limits of classical approaches. High performance was attained in the visualization and classification of: (i) pain patients (proteomic measurements) in peripheral neuropathy; (ii) human organ tissues (genomic transcription factor measurements) on the basis of their embryological origin. Conclusion: MC provides a valuable framework to estimate nonlinear distances in small datasets. Its extension to large datasets is prefigured for novel NMLs. Classification of neuropathic pain by proteomic profiles offers new insights for future molecular and systems biology characterization of pain. Improvements in tissue embryological classification refine results obtained in an earlier study, and suggest a possible reinterpretation of skin attribution as mesodermal. © The Author(s) 2010. Published by Oxford University Press.

  2. A Cluster Randomized-Controlled Trial of a Classroom-Based Drama Workshop Program to Improve Mental Health Outcomes among Immigrant and Refugee Youth in Special Classes

    Rousseau, Cécile; Beauregard, Caroline; Daignault, Katherine; Petrakos, Harriet; Thombs, Brett D.; Steele, Russell; Vasiliadis, Helen-Maria; Hechtman, Lily

    2014-01-01

    Objectives The aim of this cluster randomized trial was to evaluate the effectiveness of a school-based theatre intervention program for immigrant and refugee youth in special classes for improving mental health and academic outcomes. The primary hypothesis was that students in the theatre intervention group would report a greater reduction in impairment from symptoms compared to students in the control and tutoring groups. Methods Special classrooms in five multiethnic high schools were rand...

  3. Differences in the expressed HLA class I alleles effect the differential clustering of HIV type 1-specific T cell responses in infected Chinese and Caucasians

    Yu,XG; Addo,MM; Perkins,BA; Wej,FL; Rathod,A; Geer,SC; Parta,M; Cohen,D; Stone,DR; Russell,CJ; Tanzi,G; Mei,S; Wureel,AG; Frahm,N; Lichterfeld,M; Heath,L; Mullins,JI; Marincola,F; Goulder,PJR; Brander,C; Allen,T; Cao,YZ; Walker,BD; Altfeld,M

    2005-01-01

    China is a region of the world with a rapidly spreading HIV-1 epidemic. Studies providing insights into HIV-1 pathogenesis in infected Chinese are urgently needed to support the design and testing of an effective HIV-1 vaccine for this population. HIV-1-specific T cell responses were characterized in 32 HIV-1-infected individuals of Chinese origin and compared to 34 infected caucasians using 410 overlapping peptides spanning the entire HIV-1 clade B consensus sequence in an IFN-gamma ELISpot assay. All HIV-1 proteins were targeted with similar frequency in both populations and all study subjects recognized at least one overlapping peptide. HIV-1-specific T cell responses clustered in seven different regions of the HIV-1 genome in the Chinese cohort and in nine different regions in the caucasian cohort. The dominant HLA class I alleles expressed in the two populations differed significantly, and differences in epitope clustering pattern were shown to be influenced by differences in class I alleles that restrict immunodominant epitopes. These studies demonstrate that the clustering of HIV-1-specific T cell responses is influenced by the genetic HLA class I background in the study populations. The design and testing of candidate vaccines to fight the rapidly growing HIV-1 epidemic must therefore take the HLA genetics of the population into account as specific regions of the virus can be expected to be differentially targeted in ethnically diverse populations.

  4. OptimClass: Using species-to-cluster fidelity to determine the optimal partition in classification of ecological communities

    Tichý, L.; Chytrý, M.; Hájek, Michal; Talbot, S. S.; Botta-Dukát, Z.

    2010-01-01

    Roč. 21, č. 2 (2010), s. 287-299. ISSN 1100-9233 R&D Projects: GA ČR GA206/09/0329 Institutional research plan: CEZ:AV0Z60050516 Keywords : cluster analysis * cover transformation * dendrogram Subject RIV: EF - Botanics Impact factor: 2.457, year: 2010

  5. Clustering and classification

    Arabie, Phipps

    1996-01-01

    At a moderately advanced level, this book seeks to cover the areas of clustering and related methods of data analysis where major advances are being made. Topics include: hierarchical clustering, variable selection and weighting, additive trees and other network models, relevance of neural network models to clustering, the role of computational complexity in cluster analysis, latent class approaches to cluster analysis, theory and method with applications of a hierarchical classes model in psychology and psychopathology, combinatorial data analysis, clusterwise aggregation of relations, review

  6. Cluster automorphism groups of cluster algebras with coefficients

    Chang, Wen; Zhu, Bin

    2015-01-01

    We study the cluster automorphism group of a skew-symmetric cluster algebra with geometric coefficients. For this, we introduce the notion of gluing free cluster algebra, and show that under a weak condition the cluster automorphism group of a gluing free cluster algebra is a subgroup of the cluster automorphism group of its principal part cluster algebra (i.e. the corresponding cluster algebra without coefficients). We show that several classes of cluster algebras with coefficients are gluin...

  7. A cluster randomized-controlled trial of a classroom-based drama workshop program to improve mental health outcomes among immigrant and refugee youth in special classes.

    Cécile Rousseau

    Full Text Available The aim of this cluster randomized trial was to evaluate the effectiveness of a school-based theatre intervention program for immigrant and refugee youth in special classes for improving mental health and academic outcomes. The primary hypothesis was that students in the theatre intervention group would report a greater reduction in impairment from symptoms compared to students in the control and tutoring groups.Special classrooms in five multiethnic high schools were randomly assigned to theater intervention (n = 10, tutoring (n = 10 or control status (n = 9, for a total of 477 participants. Students and teachers were non-blinded to group assignment. The primary outcome was impairment from emotional and behavioural symptoms assessed by the Impact Supplement of the Strengths and Difficulties Questionnaire (SDQ completed by the adolescents. The secondary outcomes were the SDQ global scores (teacher and youth reports, impairment assessed by teachers and school performance. The effect of the interventions was assessed through linear mixed effect models which incorporate the correlation between students in the same class, due to the nature of the randomization of the interventions by classroom.The theatre intervention was not associated with a greater reduction in self-reported impairment and symptoms in youth placed in special class because of learning, emotional and behavioural difficulties than a tutoring intervention or a non-active control group. The estimates of the different models show a non-significant decrease in both self-reported and impairment scores in the theatre intervention group for the overall group, but the impairment score decreased significantly for first generation adolescents while it increased for second generation adolescents.The difference between the population of immigrant and refugee youth newcomers studied previously and the sample of this trial may explain some of the differences in the observed impact of

  8. Fuzzy Clustering

    Berks, G.; Keyserlingk, Diedrich Graf von; Jantzen, Jan;

    2000-01-01

    and clustering are the basic concerns in medicine. Classification depends on definitions of the classes and their required degree of participant of the elements in the cases' symptoms. In medicine imprecise conditions are the rule and therefore fuzzy methods are much more suitable than crisp ones...

  9. Implementation and experimental analysis of consensus clustering

    Perc, Domen

    2011-01-01

    Consensus clustering is a machine learning tehnique for class discovery and clustering validation. The method uses various clustering algorithms in conjunction with different resampling tehniques for data clustering. It is based on multiple runs of clustering and sampling algorithm. Data gathered in these runs is used for clustering and for visual representation of clustering. Visual representation helps us to understand clustering results. In this thesis we compare consensus clustering with ...

  10. Cluster categories and cluster-tilted algebras

    Torkildsen, Hermund Andre

    2006-01-01

    We have given an introduction to the theory of cluster categories and cluster-tilted algebras, and this was one of our main objectives in this thesis. We have seen that cluster-tilted algebras are relation-extension algebras, and this gave us a way of constructing the quiver of a cluster-tilted algebra from a tilted algebra. A cluster-tilted algebra of finite representation type is determined by its quiver, and this raised questions about the generality of this result. We defined a new class...

  11. Meaningful Effect Sizes, Intra-Class Correlations, and Proportions of Variance Explained by Covariates for Planning 3 Level Cluster Randomized Experiments in Prevention Science

    Dong, Nianbo; Reinke, Wendy M.; Herman, Keith C.; Bradshaw, Catherine P.; Murray, Desiree W.

    2015-01-01

    Cluster randomized experiments are now widely used to examine intervention effects in prevention science. It is meaningful to use empirical benchmarks for interpreting effect size in prevention science. The effect size (i.e., the standardized mean difference, calculated by the difference of the means between the treatment and control groups,…

  12. Clustering high dimensional data

    Assent, Ira

    2012-01-01

    for clustering are required. Consequently, recent research has focused on developing techniques and clustering algorithms specifically for high-dimensional data. Still, open research issues remain. Clustering is a data mining task devoted to the automatic grouping of data based on mutual similarity. Each cluster......High-dimensional data, i.e., data described by a large number of attributes, pose specific challenges to clustering. The so-called ‘curse of dimensionality’, coined originally to describe the general increase in complexity of various computational problems as dimensionality increases, is known...... groups objects that are similar to one another, whereas dissimilar objects are assigned to different clusters, possibly separating out noise. In this manner, clusters describe the data structure in an unsupervised manner, i.e., without the need for class labels. A number of clustering paradigms exist...

  13. Crossings in Clustered Level Graphs

    Forster, Michael

    2005-01-01

    Clustered graphs are an enhanced graph model with a recursive clustering of the vertices according to a given nesting relation. This prime technique for expressing coherence of certain parts of the graph is used in many applications, such as biochemical pathways and UML class diagrams. For directed clustered graphs usually level drawings are used, leading to clustered level graphs. In this thesis we analyze the interrelation of clusters and levels and their influence on edge crossings and clu...

  14. Deep observations of the Super-CLASS super-cluster at 325 MHz with the GMRT: the low-frequency source catalogue

    Riseley, C J; Hales, C A; Harrison, I; Birkinshaw, M; Battye, R A; Beswick, R J; Brown, M L; Casey, C M; Chapman, S C; Demetroullas, C; Hung, C -L; Jackson, N J; Muxlow, T; Watson, B

    2016-01-01

    We present the results of 325 MHz GMRT observations of a super-cluster field, known to contain five Abell clusters at redshift $z \\sim 0.2$. We achieve a nominal sensitivity of $34\\,\\mu$Jy beam$^{-1}$ toward the phase centre. We compile a catalogue of 3257 sources with flux densities in the range $183\\,\\mu\\rm{Jy}\\,-\\,1.5\\,\\rm{Jy}$ within the entire $\\sim 6.5$ square degree field of view. Subsequently, we use available survey data at other frequencies to derive the spectral index distribution for a sub-sample of these sources, recovering two distinct populations -- a dominant population which exhibit spectral index trends typical of steep-spectrum synchrotron emission, and a smaller population of sources with typically flat or rising spectra. We identify a number of sources with ultra-steep spectra or rising spectra for further analysis, finding two candidate high-redshift radio galaxies and three gigahertz-peaked-spectrum radio sources. Finally, we derive the Euclidean-normalised differential source counts us...

  15. Clustering signatures classify directed networks

    Ahnert, S. E.; Fink, T. M. A.

    2008-09-01

    We use a clustering signature, based on a recently introduced generalization of the clustering coefficient to directed networks, to analyze 16 directed real-world networks of five different types: social networks, genetic transcription networks, word adjacency networks, food webs, and electric circuits. We show that these five classes of networks are cleanly separated in the space of clustering signatures due to the statistical properties of their local neighborhoods, demonstrating the usefulness of clustering signatures as a classifier of directed networks.

  16. A vision for growing a world-class power technology cluster in a smart, sustainable British Columbia : full report to the Premier's Technology Council

    This report presents a framework for power technology in British Columbia and the development of new sources of energy while ensuring the sustainable economic growth. It also explores the opportunities present in the power technology sector. A definition of the power technology industry was provided, and market drivers were identified, describing the region's competitive advantage and assets. Five market opportunities were introduced, comprising the report's targeted innovation strategy: remote power solutions; sustainable urban practices; smart transport; smart grid; and large scale clean green power production. An outline of the current energy market in British Columbia was presented with details of research and development in renewable energy sources. Global power demands were also outlined. A regional action plan was presented in order to develop the power technology cluster. Leadership strategies were presented, with economic development goals and working teams geared towards an implementation resource plan. A commercialization strategy was suggested in order to address local demand, commercialization funds, and increasing access and resources. A growth strategy was also presented to assist in the development of access to world markets, create partnerships and assist in branding and collaborations with industry and government. An innovation strategy was outlined, with the aim of developing research initiatives, support centres in key market and technology areas and connecting existing efforts in basic sciences to power technology applications. It was concluded that in order to achieve full implementation of these strategies, a short term task force is necessary to shape overall plans. Additionally, an ongoing vision team, working groups and coordination is necessary to implement overall strategies and subcomponents. Appendices were included with reference to each of the five market opportunities presented in the report. 58 refs

  17. Projection effects in cluster catalogues

    Van Haarlem, M P; White, S D M

    1997-01-01

    We investigate the importance of projection effects in the identification of galaxy clusters in 2D galaxy maps and their effect on the estimation of cluster velocity dispersions. A volume limited galaxy catalogue that was derived from a Standard CDM N-body simulation was used. We select clusters using criteria that match those employed in the construction of real cluster catalogues and find that our mock Abell cluster catalogues are heavily contaminated and incomplete. Over one third (34 per cent) of clusters of richness class R>=1 are miclassifications arising from the projection of one or more clumps onto an intrinsically poor cluster. Conversely, 32 per cent of intrinsically rich clusters are missed from the R>=1 catalogues, mostly because of statistical fluctuations in the background count. Selection by X-ray luminosity rather than optical richness reduces, but does not completely eliminate, these problems. Contamination by unvirialised sub-clumps near a cluster leads to a considerable overestimation of t...

  18. Voltage Graphs and Cluster Consensus with Point Group Symmetries

    Chen, Xudong; Belabbas, Mohamed-Ali; Basar, Tamer

    2016-01-01

    A cluster consensus system is a multi-agent system in which the autonomous agents communicate to form multiple clusters, with each cluster of agents asymptotically converging to the same clustering point. We introduce in this paper a special class of cluster consensus dynamics, termed the $G$-clustering dynamics for $G$ a point group, whereby the autonomous agents can form as many as $|G|$ clusters, and moreover, the associated $|G|$ clustering points exhibit a geometric symmetry induced by t...

  19. Cluster Automorphisms

    Assem, Ibrahim; Schiffler, Ralf; Shramchenko, Vasilisa

    2010-01-01

    In this article, we introduce the notion of cluster automorphism of a given cluster algebra as a $\\ZZ$-automorphism of the cluster algebra that sends a cluster to another and commutes with mutations. We study the group of cluster automorphisms in detail for acyclic cluster algebras and cluster algebras from surfaces, and we compute this group explicitly for the Dynkin types and the Euclidean types.

  20. Tune Your Brown Clustering, Please

    Derczynski, Leon; Chester, Sean; Bøgh, Kenneth Sejdenfaden

    2015-01-01

    Brown clustering, an unsupervised hierarchical clustering technique based on ngram mutual information, has proven useful in many NLP applications. However, most uses of Brown clustering employ the same default configuration; the appropriateness of this configuration has gone predominantly...... unexplored. Accordingly, we present information for practitioners on the behaviour of Brown clustering in order to assist hyper-parametre tuning, in the form of a theoretical model of Brown clustering utility. This model is then evaluated empirically in two sequence labelling tasks over two text types. We...... explore the dynamic between the input corpus size, chosen number of classes, and quality of the resulting clusters, which has an impact for any approach using Brown clustering. In every scenario that we examine, our results reveal that the values most commonly used for the clustering are sub-optimal....

  1. Cluster Structure on Generalized Weyl Algebras

    Saleh, Ibrahim

    2011-01-01

    We introduce a class of non-commutative algebras that carry a non-commutative (geometric) cluster structure which are generated by identical copies of generalized Weyl algebras. Equivalent conditions for the finiteness of the set of the cluster variables of these cluster structures are provided. Some combinatorial data, called \\textit{cluster strands,} arising from the cluster structure are used to construct irreducible representations of generalized Weyl algebras.

  2. Clustering of attribute and/or relational data:

    Ferligoj, Anuška; Kronegger, Luka

    2009-01-01

    A large class of clustering problems can be formulated as an optimizational problem in which the best clustering is searched for among all feasible clustering according to a selected criterion function. This clustering approach can be applied to a variety of very interesting clustering problems, as it is possible to adapt it to a concrete clustering problem by an appropriate specification of the criterion function and/or by the definition of the set of feasible clusterings. Both, the blockmod...

  3. Possibilistic clustering for shape recognition

    Keller, James M.; Krishnapuram, Raghu

    1993-01-01

    Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, the clustering problem was cast into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data was constructed, and the membership and prototype update equations from necessary conditions for minimization of our criterion function were derived. The ability of this approach to detect linear and quartic curves in the presence of considerable noise is shown.

  4. Weighted Clustering

    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...

  5. Cluster Headache

    Frederick G Freitag

    1985-01-01

    Learning Objectives: Review the current understanding of the pathophysiology of cluster headache Be able to recognize the clinical features of cluster headache Be able to develop a strategy for treatment of cluster headache Cluster headache is divided into multiple subtypes under the IHC classification criteria. The vast majority of patients present with episodic cluster headache (3.1.1). This will be the focus of the presentation. The syndrome is characterized by repeated at...

  6. Class size versus class composition

    Jones, Sam

    Raising schooling quality in low-income countries is a pressing challenge. Substantial research has considered the impact of cutting class sizes on skills acquisition. Considerably less attention has been given to the extent to which peer effects, which refer to class composition, also may affect...

  7. Yellow supergiants in open clusters

    Superluminous giant stars (SLGs) have been reported in young globular clusters in the Large Magellanic Cloud (LMC). These stars appear to be in the post-asymptotic-giant-branch phase of evolution. This program was an investigation of galactic SLG candidates in open clusters, which are more like the LMC young globular clusters. These were chosen because luminosity, mass, and age determinations can be made for members since cluster distances and interstellar reddenings are known. Color magnitude diagrams were searched for candidates, using the same selection criteria as for SLGs in the LMC. Classification spectra were obtained of 115 program stars from McGraw-Hill Observatory and of 68 stars from Cerro Tololo Inter-American Observatory Chile. These stars were visually classified on the MK system using spectral scans of standard stars taken at the respective observations. Published information was combined with this program's data for 83 stars in 30 clusters. Membership probabilities were assigned to these stars, and the clusters were analyzed according to age. It was seen that the intrinsically brightest supergiants are found in the youngest clusters. With increasing cluster age, the absolute luminosities attained by the supergiants decline. Also, it appears that the evolutionary tracks of luminosity class II stars are more similar to those of class I than of class III

  8. Word classes

    Rijkhoff, Jan

    2007-01-01

    This article provides an overview of recent literature and research on word classes, focusing in particular on typological approaches to word classification. The cross-linguistic classification of word class systems (or parts-of-speech systems) presented in this article is based on statements found...... in grammatical descriptions of some 50 languages, which together constitute a representative sample of the world’s languages (Hengeveld et al. 2004: 529). It appears that there are both quantitative and qualitative differences between word class systems of individual languages. Whereas some languages...... employ a parts-of-speech system that includes the categories Verb, Noun, Adjective and Adverb, other languages may use only a subset of these four lexical categories. Furthermore, quite a few languages have a major word class whose members cannot be classified in terms of the categories Verb – Noun...

  9. Clustering via Kernel Decomposition

    Have, Anna Szynkowiak; Girolami, Mark A.; Larsen, Jan

    2006-01-01

    Methods for spectral clustering have been proposed recently which rely on the eigenvalue decomposition of an affinity matrix. In this work it is proposed that the affinity matrix is created based on the elements of a non-parametric density estimator. This matrix is then decomposed to obtain...... posterior probabilities of class membership using an appropriate form of nonnegative matrix factorization. The troublesome selection of hyperparameters such as kernel width and number of clusters can be obtained using standard cross-validation methods as is demonstrated on a number of diverse data sets....

  10. Cluster headache

    Histamine headache; Headache - histamine; Migrainous neuralgia; Headache - cluster; Horton's headache ... Doctors do not know exactly what causes cluster headaches. They ... (chemical in the body released during an allergic response) or ...

  11. Isotopic clusters

    Spectra of isotopically mixed clusters (dimers of SF6) are calculated as well as transition frequencies. The result leads to speculations about the suitability of the laser-cluster fragmentation process for isotope separation. (Auth.)

  12. Weighted Clustering

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

    2012-01-01

    We investigate a natural generalization of the classical clusteringproblem, considering clustering tasks in which differentinstances may have different weights.We conduct the firstextensive theoretical analysis on the influence of weighteddata on standard clustering algorithms in both the partitionaland hierarchical settings, characterizing the conditions underwhich algorithms react to weights. Extending a recent frameworkfor clustering algorithm selection, we propose intuitiveproperties that...

  13. Meaningful Clusters

    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.

  14. A possibilistic approach to clustering

    Krishnapuram, Raghu; Keller, James M.

    1993-01-01

    Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

  15. Graph partitioning advance clustering technique

    Madhulatha, T Soni

    2012-01-01

    Clustering is a common technique for statistical data analysis, Clustering is the process of grouping the data into classes or clusters so that objects within a cluster have high similarity in comparison to one another, but are very dissimilar to objects in other clusters. Dissimilarities are assessed based on the attribute values describing the objects. Often, distance measures are used. Clustering is an unsupervised learning technique, where interesting patterns and structures can be found directly from very large data sets with little or none of the background knowledge. This paper also considers the partitioning of m-dimensional lattice graphs using Fiedler's approach, which requires the determination of the eigenvector belonging to the second smallest Eigenvalue of the Laplacian with K-means partitioning algorithm.

  16. Cluster Lenses

    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...

  17. Evaluating Mixture Modeling for Clustering: Recommendations and Cautions

    Steinley, Douglas; Brusco, Michael J.

    2011-01-01

    This article provides a large-scale investigation into several of the properties of mixture-model clustering techniques (also referred to as latent class cluster analysis, latent profile analysis, model-based clustering, probabilistic clustering, Bayesian classification, unsupervised learning, and finite mixture models; see Vermunt & Magdison,…

  18. Data Clustering

    Wagstaff, Kiri L.

    2012-03-01

    On obtaining a new data set, the researcher is immediately faced with the challenge of obtaining a high-level understanding from the observations. What does a typical item look like? What are the dominant trends? How many distinct groups are included in the data set, and how is each one characterized? Which observable values are common, and which rarely occur? Which items stand out as anomalies or outliers from the rest of the data? This challenge is exacerbated by the steady growth in data set size [11] as new instruments push into new frontiers of parameter space, via improvements in temporal, spatial, and spectral resolution, or by the desire to "fuse" observations from different modalities and instruments into a larger-picture understanding of the same underlying phenomenon. Data clustering algorithms provide a variety of solutions for this task. They can generate summaries, locate outliers, compress data, identify dense or sparse regions of feature space, and build data models. It is useful to note up front that "clusters" in this context refer to groups of items within some descriptive feature space, not (necessarily) to "galaxy clusters" which are dense regions in physical space. The goal of this chapter is to survey a variety of data clustering methods, with an eye toward their applicability to astronomical data analysis. In addition to improving the individual researcher’s understanding of a given data set, clustering has led directly to scientific advances, such as the discovery of new subclasses of stars [14] and gamma-ray bursts (GRBs) [38]. All clustering algorithms seek to identify groups within a data set that reflect some observed, quantifiable structure. Clustering is traditionally an unsupervised approach to data analysis, in the sense that it operates without any direct guidance about which items should be assigned to which clusters. There has been a recent trend in the clustering literature toward supporting semisupervised or constrained

  19. Cluster Chemistry

    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.

  20. Clustering by Pattern Similarity

    Hai-xun Wang; Jian Pei

    2008-01-01

    The task of clustering is to identify classes of similar objects among a set of objects. The definition of similarity varies from one clustering model to another. However, in most of these models the concept of similarity is often based on such metrics as Manhattan distance, Euclidean distance or other Lp distances. In other words, similar objects must have close values in at least a set of dimensions. In this paper, we explore a more general type of similarity. Under the pCluster model we proposed, two objects are similar if they exhibit a coherent pattern on a subset of dimensions. The new similarity concept models a wide range of applications. For instance, in DNA microarray analysis, the expression levels of two genes may rise and fall synchronously in response to a set of environmental stimuli. Although the magnitude of their expression levels may not be close, the patterns they exhibit can be very much alike. Discovery of such clusters of genes is essential in revealing significant connections in gene regulatory networks. E-commerce applications, such as collaborative filtering, can also benefit from the new model, because it is able to capture not only the closeness of values of certain leading indicators but also the closeness of (purchasing, browsing, etc.) patterns exhibited by the customers. In addition to the novel similarity model, this paper also introduces an effective and efficient algorithm to detect such clusters, and we perform tests on several real and synthetic data sets to show its performance.

  1. COMPARISON OF PURITY AND ENTROPY OF K-MEANS CLUSTERING AND FUZZY C MEANS CLUSTERING

    Satya Chaitanya Sripada

    2011-06-01

    Full Text Available Clustering is one the main area in data mining literature. There are various algorithms for clustering. The evaluation of the performance isdone by validation measures. The external validation measures are used to measure the extent to which cluster labels affirm with theexternally given class labels. The aim of this paper is to compare the for K-means and Fuzzy C means clustering using the Purity andEntropy. The data used for evaluating the external measures is medical data.

  2. Class distinction

    White, M. Catherine

    Typical 101 courses discourage many students from pursuing higher level science and math courses. Introductory classes in science and math serve largely as a filter, screening out all but the most promising students, and leaving the majority of college graduates—including most prospective teachers—with little understanding of how science works, according to a study conducted for the National Science Foundation. Because few teachers, particularly at the elementary level, experience any collegiate science teaching that stresses skills of inquiry and investigation, they simply never learn to use those methods in their teaching, the report states.

  3. Cancer Clusters

    ... of cancer. Cancer clusters can help scientists identify cancer-causing substances in the environment. For example, in the early 1970s, a cluster ... the area and time period over which the cancers were diagnosed. They also ask about specific environmental hazards or concerns in the affected area. If ...

  4. Clustering processes

    Ryabko, Daniil

    2010-01-01

    The problem of clustering is considered, for the case when each data point is a sample generated by a stationary ergodic process. We propose a very natural asymptotic notion of consistency, and show that simple consistent algorithms exist, under most general non-parametric assumptions. The notion of consistency is as follows: two samples should be put into the same cluster if and only if they were generated by the same distribution. With this notion of consistency, clustering generalizes such classical statistical problems as homogeneity testing and process classification. We show that, for the case of a known number of clusters, consistency can be achieved under the only assumption that the joint distribution of the data is stationary ergodic (no parametric or Markovian assumptions, no assumptions of independence, neither between nor within the samples). If the number of clusters is unknown, consistency can be achieved under appropriate assumptions on the mixing rates of the processes. (again, no parametric ...

  5. Attracting higher income class to public transport in socially clustered cities. The case of Caracas. / Atracción del mayor nivel de ingresos al transporte público en las ciudades socialmente segregadas. El caso de Caracas.

    Flórez, Josefina

    2000-03-01

    Full Text Available In Caracas, as in most socially clustered cities, modal split is highly related to income. High income population is mostly car dependant, while lower income people are captive of public transport. This typical situation is explained by world-wide social values and fashion but also by the fact that new, segregated residential areas for the upper social levels have been located in areas poorly served by public transport, creating a dependency on the private car. It is not surprising that, during the 1970's, a high proportion of Caracas's middle and high-income citizens were systematically using their car even in areas where there was a good offer of public transport. What is more unusual is to realise that, since 1983 when the metro system was inaugurated, there is a new pattern of travel behaviour. The metro has mainly attracted high-income people. Besides the few of them who have transferred from surface to underground public transport, many of the wealthier patrons seem to be regular car users that presently take the metro when it provides a good alternative. Currently, the transit system in Caracas is comprised of four main modes: the metro (since 1983; the "por puesto", which are minibus vehicles of 18 to 32 seats; the jeeps, which are dual traction vehicles of up to 12 seat (most of them serving hilly areas, basically slums; and the bus system, consisting of metro-bus and private operators. CA Metro operates the metro and, since 1987, metro-bus lines, which are bus feeder services to its heavy rail metro operation that extend the cover area of the system into the less central zones of the city. While the metro and metro-bus offer transit services to middle and high income users, the mini-buses and jeeps provide flexible transit service to low income groups. The metro and metro-bus services are more reliable and offer higher quality that mini-buses and jeeps. This higher quality service is one of the main attributes attracting the wealthier

  6. Clustering analysis

    Cluster analysis is the name of group of multivariate techniques whose principal purpose is to distinguish similar entities from the characteristics they process.To study this analysis, there are several algorithms that can be used. Therefore, this topic focuses to discuss the algorithms, such as, similarity measures, and hierarchical clustering which includes single linkage, complete linkage and average linkage method. also, non-hierarchical clustering method, which is popular name K-mean method' will be discussed. Finally, this paper will be described the advantages and disadvantages of every methods

  7. Cluster editing

    Böcker, S.; Baumbach, Jan

    2013-01-01

    The Cluster Editing problem asks to transform a graph into a disjoint union of cliques using a minimum number of edge modifications. Although the problem has been proven NP-complete several times, it has nevertheless attracted much research both from the theoretical and the applied side. The...... algorithms for biological problems. © 2013 Springer-Verlag....... problem has been the inspiration for numerous algorithms in bioinformatics, aiming at clustering entities such as genes, proteins, phenotypes, or patients. In this paper, we review exact and heuristic methods that have been proposed for the Cluster Editing problem, and also applications of these...

  8. Cluster analysis

    Everitt, Brian S; Leese, Morven; Stahl, Daniel

    2011-01-01

    Cluster analysis comprises a range of methods for classifying multivariate data into subgroups. By organizing multivariate data into such subgroups, clustering can help reveal the characteristics of any structure or patterns present. These techniques have proven useful in a wide range of areas such as medicine, psychology, market research and bioinformatics.This fifth edition of the highly successful Cluster Analysis includes coverage of the latest developments in the field and a new chapter dealing with finite mixture models for structured data.Real life examples are used throughout to demons

  9. Spitzer Clusters

    Krick, Kessica

    This proposal is a specific response to the strategic goal of NASA's research program to "discover how the universe works and explore how the universe evolved into its present form." Towards this goal, we propose to mine the Spitzer archive for all observations of galaxy groups and clusters for the purpose of studying galaxy evolution in clusters, contamination rates for Sunyaev Zeldovich cluster surveys, and to provide a database of Spitzer observed clusters to the broader community. Funding from this proposal will go towards two years of support for a Postdoc to do this work. After searching the Spitzer Heritage Archive, we have found 194 unique galaxy groups and clusters that have data from both the Infrared array camera (IRAC; Fazio et al. 2004) at 3.6 - 8 microns and the multiband imaging photometer for Spitzer (MIPS; Rieke et al. 2004) at 24microns. This large sample will add value beyond the individual datasets because it will be a larger sample of IR clusters than ever before and will have sufficient diversity in mass, redshift, and dynamical state to allow us to differentiate amongst the effects of these cluster properties. An infrared sample is important because it is unaffected by dust extinction while at the same time is an excellent measure of both stellar mass (IRAC wavelengths) and star formation rate (MIPS wavelengths). Additionally, IRAC can be used to differentiate star forming galaxies (SFG) from active galactic nuclei (AGN), due to their different spectral shapes in this wavelength regime. Specifically, we intend to identify SFG and AGN in galaxy groups and clusters. Groups and clusters differ from the field because the galaxy densities are higher, there is a large potential well due mainly to the mass of the dark matter, and there is hot X-ray gas (the intracluster medium; ICM). We will examine the impact of these differences in environment on galaxy formation by comparing cluster properties of AGN and SFG to those in the field. Also, we will

  10. Cluster Bulleticity

    Massey, Richard; Kitching, Thomas D.; Nagai, Daisuke

    2010-01-01

    The unique properties of dark matter are revealed during collisions between clusters of galaxies, like the bullet cluster (1E 0657-56) and baby bullet (MACSJ0025-12). These systems provide evidence for an additional, invisible mass in the separation between the distribution of their total mass, measured via gravitational lensing, and their ordinary 'baryonic' matter, measured via its X-ray emission. Unfortunately, the information available from these systems is limited by th...

  11. Cluster Bulleticity

    Massey, R; Kitching, T.; Nagai, D.

    2010-01-01

    The unique properties of dark matter are revealed during collisions between clusters of galaxies, such as the bullet cluster (1E 0657−56) and baby bullet (MACS J0025−12). These systems provide evidence for an additional, invisible mass in the separation between the distributions of their total mass, measured via gravitational lensing, and their ordinary ‘baryonic’ matter, measured via its X-ray emission. Unfortunately, the information available from these systems is limited by their rarity. C...

  12. Cluster generator

    Donchev, Todor I.; Petrov, Ivan G.

    2011-05-31

    Described herein is an apparatus and a method for producing atom clusters based on a gas discharge within a hollow cathode. The hollow cathode includes one or more walls. The one or more walls define a sputtering chamber within the hollow cathode and include a material to be sputtered. A hollow anode is positioned at an end of the sputtering chamber, and atom clusters are formed when a gas discharge is generated between the hollow anode and the hollow cathode.

  13. Euclidean Distances, soft and spectral Clustering on Weighted Graphs

    Bavaud, François

    2010-01-01

    We define a class of Euclidean distances on weighted graphs, enabling to perform thermodynamic soft graph clustering. The class can be constructed form the "raw coordinates" encountered in spectral clustering, and can be extended by means of higher-dimensional embeddings (Schoenberg transformations). Geographical flow data, properly conditioned, illustrate the procedure as well as visualization aspects.

  14. Poincare Invariance, Cluster Properties, and Particle Production

    Polyzou, W. N.

    2002-01-01

    A method is presented for constructing a class of Poincare invariant quantum mechanical models of systems of a finite number of degrees of freedom that satisfy cluster separability, the spectral condition, but do not conserve particle number. The class of models includes the relativistic Lee model and relativistic isobar models.

  15. M$_1$ - M* correlation in galaxy clusters

    Trevese, D; Appodia, B

    1994-01-01

    Photographic F band photometry of a sample of 36 Abell clusters has been used to study the relation between the magnitude M_1 of the brightest cluster member and the Schechter function parameter M^*. Clusters appear segregated in the M_1-M^* plane according to their Rood \\& Sastry class. We prove on a statistical basis that on average, going from early to late RS classes, M_1 becomes brighter while M^* becomes fainter. The result agrees with the predictions of galactic cannibalism models, never confirmed by previous analyses.

  16. Denominators of cluster variables

    Buan, Aslak Bakke; Marsh, Robert J.; Reiten, Idun

    2007-01-01

    Associated to any acyclic cluster algebra is a corresponding triangulated category known as the cluster category. It is known that there is a one-to-one correspondence between cluster variables in the cluster algebra and exceptional indecomposable objects in the cluster category inducing a correspondence between clusters and cluster-tilting objects. Fix a cluster-tilting object T and a corresponding initial cluster. By the Laurent phenomenon, every cluster variable can be written as a Laurent...

  17. Validity Index and number of clusters

    Mohamed Fadhel Saad

    2012-01-01

    Full Text Available Clustering (or cluster analysis has been used widely in pattern recognition, image processing, and data analysis. It aims to organize a collection of data items into c clusters, such that items within a cluster are more similar to each other than they are items in the other clusters. The number of clusters c is the most important parameter, in the sense that the remaining parameters have less influence on the resulting partition. To determine the best number of classes several methods were made, and are called validity index. This paper presents a new validity index for fuzzy clustering called a Modified Partition Coefficient And Exponential Separation (MPCAES index. The efficiency of the proposed MPCAES index is compared with several popular validity indexes. More information about these indexes is acquired in series of numerical comparisons and also real data Iris.

  18. High Dimensional Data Clustering Using Fast Cluster Based Feature Selection

    Karthikeyan.P

    2014-03-01

    Full Text Available Feature selection involves identifying a subset of the most useful features that produces compatible results as the original entire set of features. A feature selection algorithm may be evaluated from both the efficiency and effectiveness points of view. While the efficiency concerns the time required to find a subset of features, the effectiveness is related to the quality of the subset of features. Based on these criteria, a fast clustering-based feature selection algorithm (FAST is proposed and experimentally evaluated in this paper. The FAST algorithm works in two steps. In the first step, features are divided into clusters by using graph-theoretic clustering methods. In the second step, the most representative feature that is strongly related to target classes is selected from each cluster to form a subset of features. Features in different clusters are relatively independent; the clustering-based strategy of FAST has a high probability of producing a subset of useful and independent features. To ensure the efficiency of FAST, we adopt the efficient minimum-spanning tree (MST using the Kruskal‟s Algorithm clustering method. The efficiency and effectiveness of the FAST algorithm are evaluated through an empirical study. Index Terms—

  19. Cluster-lensing: A Python Package for Galaxy Clusters & Miscentering

    Ford, Jes

    2016-01-01

    We describe a new open source package for calculating properties of galaxy clusters, including NFW halo profiles with and without the effects of cluster miscentering. This pure-Python package, cluster-lensing, provides well-documented and easy-to-use classes and functions for calculating cluster scaling relations, including mass-richness and mass-concentration relations from the literature, as well as the surface mass density $\\Sigma(R)$ and differential surface mass density $\\Delta\\Sigma(R)$ profiles, probed by weak lensing magnification and shear. Galaxy cluster miscentering is especially a concern for stacked weak lensing shear studies of galaxy clusters, where offsets between the assumed and the true underlying matter distribution can lead to a significant bias in the mass estimates if not accounted for. This software has been developed and released in a public GitHub repository, and is licensed under the permissive MIT license. The cluster-lensing package is archived on Zenodo (Ford 2016). Full documenta...

  20. EM Clustering Analysis of Diabetes Patients Basic Diagnosis Index

    Wu, Cai; Steinbauer, Jeffrey R.; Kuo, Grace M

    2005-01-01

    Cluster analysis can group similar instances into same group. Partitioning cluster assigns classes to samples without known the classes in advance. Most common algorithms are K-means and Expectation Maximization (EM). EM clustering algorithm can find number of distributions of generating data and build “mixture models”. It identifies groups that are either overlapping or varying sizes and shapes. In this project, by using EM in Machine Learning Algorithm in JAVA (WEKA) syste...

  1. Cluster Bulleticity

    Massey, Richard; Nagai, Daisuke

    2010-01-01

    The unique properties of dark matter are revealed during collisions between clusters of galaxies, like the bullet cluster (1E 0657-56) and baby bullet (MACSJ0025-12). These systems provide evidence for an additional, invisible mass in the separation between the distribution of their total mass, measured via gravitational lensing, and their ordinary 'baryonic' matter, measured via its X-ray emission. Unfortunately, the information available from these systems is limited by their rarity. Constraints on the properties of dark matter, such as its interaction cross-section, are therefore restricted by uncertainties in the individual systems' impact velocity, impact parameter and orientation with respect to the line of sight. Here we develop a complementary, statistical measurement in which every piece of substructure falling into every massive cluster is treated as a bullet. We define 'bulleticity' as the mean separation between dark matter and ordinary matter, and we measure a positive signal in hydrodynamical si...

  2. Evolution of major histocompatibility complex class I and class II genes in the brown bear

    Kuduk Katarzyna

    2012-10-01

    Full Text Available Abstract Background Major histocompatibility complex (MHC proteins constitute an essential component of the vertebrate immune response, and are coded by the most polymorphic of the vertebrate genes. Here, we investigated sequence variation and evolution of MHC class I and class II DRB, DQA and DQB genes in the brown bear Ursus arctos to characterise the level of polymorphism, estimate the strength of positive selection acting on them, and assess the extent of gene orthology and trans-species polymorphism in Ursidae. Results We found 37 MHC class I, 16 MHC class II DRB, four DQB and two DQA alleles. We confirmed the expression of several loci: three MHC class I, two DRB, two DQB and one DQA. MHC class I also contained two clusters of non-expressed sequences. MHC class I and DRB allele frequencies differed between northern and southern populations of the Scandinavian brown bear. The rate of nonsynonymous substitutions (dN exceeded the rate of synonymous substitutions (dS at putative antigen binding sites of DRB and DQB loci and, marginally significantly, at MHC class I loci. Models of codon evolution supported positive selection at DRB and MHC class I loci. Both MHC class I and MHC class II sequences showed orthology to gene clusters found in the giant panda Ailuropoda melanoleuca. Conclusions Historical positive selection has acted on MHC class I, class II DRB and DQB, but not on the DQA locus. The signal of historical positive selection on the DRB locus was particularly strong, which may be a general feature of caniforms. The presence of MHC class I pseudogenes may indicate faster gene turnover in this class through the birth-and-death process. South–north population structure at MHC loci probably reflects origin of the populations from separate glacial refugia.

  3. Document Clustering based on Topic Maps

    Rafi, Muhammad; Farooq, Amir; 10.5120/1640-2204

    2011-01-01

    Importance of document clustering is now widely acknowledged by researchers for better management, smart navigation, efficient filtering, and concise summarization of large collection of documents like World Wide Web (WWW). The next challenge lies in semantically performing clustering based on the semantic contents of the document. The problem of document clustering has two main components: (1) to represent the document in such a form that inherently captures semantics of the text. This may also help to reduce dimensionality of the document, and (2) to define a similarity measure based on the semantic representation such that it assigns higher numerical values to document pairs which have higher semantic relationship. Feature space of the documents can be very challenging for document clustering. A document may contain multiple topics, it may contain a large set of class-independent general-words, and a handful class-specific core-words. With these features in mind, traditional agglomerative clustering algori...

  4. Quotients of cluster categories

    Jorgensen, Peter

    2007-01-01

    Higher cluster categories were recently introduced as a generalization of cluster categories. This paper shows that in Dynkin types A and D, half of all higher cluster categories are actually just quotients of cluster categories. The other half can be obtained as quotients of 2-cluster categories, the "lowest" type of higher cluster categories. Hence, in Dynkin types A and D, all higher cluster phenomena are implicit in cluster categories and 2-cluster categories. In contrast, the same is not...

  5. Regional Innovation Clusters

    Small Business Administration — The Regional Innovation Clusters serve a diverse group of sectors and geographies. Three of the initial pilot clusters, termed Advanced Defense Technology clusters,...

  6. Class Vectors: Embedding representation of Document Classes

    Sachan, Devendra Singh; Kumar, Shailesh

    2015-01-01

    Distributed representations of words and paragraphs as semantic embeddings in high dimensional data are used across a number of Natural Language Understanding tasks such as retrieval, translation, and classification. In this work, we propose "Class Vectors" - a framework for learning a vector per class in the same embedding space as the word and paragraph embeddings. Similarity between these class vectors and word vectors are used as features to classify a document to a class. In experiment o...

  7. Multiway Spectral Clustering: A Margin-Based Perspective

    Zhang, Zhihua; Jordan, Michael I.

    2008-01-01

    Spectral clustering is a broad class of clustering procedures in which an intractable combinatorial optimization formulation of clustering is "relaxed" into a tractable eigenvector problem, and in which the relaxed solution is subsequently "rounded" into an approximate discrete solution to the original problem. In this paper we present a novel margin-based perspective on multiway spectral clustering. We show that the margin-based perspective illuminates both the relaxation and rounding aspect...

  8. Teachers in Class

    Van Galen, Jane

    2008-01-01

    In this article, I argue for a closer read of the daily "class work" of teachers, as posited by Reay, 1998. In developing exploratory class portraits of four teachers who occupy distinctive social positions (two from working-class homes now teaching upper-middle-class children and two from upper-middle-class homes now teaching poor children), I…

  9. Applying Machine Learning to Star Cluster Classification

    Fedorenko, Kristina; Grasha, Kathryn; Calzetti, Daniela; Mahadevan, Sridhar

    2016-01-01

    Catalogs describing populations of star clusters are essential in investigating a range of important issues, from star formation to galaxy evolution. Star cluster catalogs are typically created in a two-step process: in the first step, a catalog of sources is automatically produced; in the second step, each of the extracted sources is visually inspected by 3-to-5 human classifiers and assigned a category. Classification by humans is labor-intensive and time consuming, thus it creates a bottleneck, and substantially slows down progress in star cluster research.We seek to automate the process of labeling star clusters (the second step) through applying supervised machine learning techniques. This will provide a fast, objective, and reproducible classification. Our data is HST (WFC3 and ACS) images of galaxies in the distance range of 3.5-12 Mpc, with a few thousand star clusters already classified by humans as a part of the LEGUS (Legacy ExtraGalactic UV Survey) project. The classification is based on 4 labels (Class 1 - symmetric, compact cluster; Class 2 - concentrated object with some degree of asymmetry; Class 3 - multiple peak system, diffuse; and Class 4 - spurious detection). We start by looking at basic machine learning methods such as decision trees. We then proceed to evaluate performance of more advanced techniques, focusing on convolutional neural networks and other Deep Learning methods. We analyze the results, and suggest several directions for further improvement.

  10. Clustering experiments

    Wang, Zhengwei; Tan, Ken; Di, Zengru; Roehner, Bertrand M

    2011-01-01

    It is well known that bees cluster together in cold weather, in the process of swarming (when the ``old'' queen leaves with part of the colony) or absconding (when the queen leaves with all the colony) and in defense against intruders such as wasps or hornets. In this paper we describe a fairly different clustering process which occurs at any temperature and independently of any special stimulus or circumstance. As a matter of fact, this process is about four times faster at 28 degree Celsius than at 15 degrees. Because of its simplicity and low level of ``noise'' we think that this phenomenon can provide a means for exploring the strength of inter-individual attraction between bees or other living organisms. For instance, and at first sight fairly surprisingly, our observations showed that this attraction does also exist between bees belonging to different colonies. As this study is aimed at providing a comparative perspective, we also describe a similar clustering experiment for red fire ants.

  11. Does Class Size Matter?

    Ehrenberg, Ronald G.; Brewer, Dominic J.; Gamoran, Adam; Willms, J. Douglas

    2001-01-01

    Reports on the significance of class size to student learning. Includes an overview of class size in various countries, the importance of teacher adaptability, and the Asian paradox of large classes allied to high test scores. (MM)

  12. Factor PD-Clustering

    Gettler Summa, Mireille; Palumbo, Francesco; Tortora, Cristina

    2012-01-01

    Factorial clustering methods have been developed in recent years thanks to the improving of computational power. These methods perform a linear transformation of data and a clustering on transformed data optimizing a common criterion. Factorial PD-clustering is based on Probabilistic Distance clustering (PD-clustering). PD-clustering is an iterative, distribution free, probabilistic, clustering method. Factor PD-clustering make a linear transformation of original variables into a reduced numb...

  13. Clustering Analysis on E-commerce Transaction Based on K-means Clustering

    Xuan HUANG

    2014-02-01

    Full Text Available Based on the density, increment and grid etc, shortcomings like the bad elasticity, weak handling ability of high-dimensional data, sensitive to time sequence of data, bad independence of parameters and weak handling ability of noise are usually existed in clustering algorithm when facing a large number of high-dimensional transaction data. Making experiments by sampling data samples of the 300 mobile phones of Taobao, the following conclusions can be obtained: compared with Single-pass clustering algorithm, the K-means clustering algorithm has a high intra-class dissimilarity and inter-class similarity when analyzing e-commerce transaction. In addition, the K-means clustering algorithm has very high efficiency and strong elasticity when dealing with a large number of data items. However, clustering effects of this algorithm are affected by clustering number and initial positions of clustering center. Therefore, it is easy to show the local optimization for clustering results. Therefore, how to determine clustering number and initial positions of the clustering center of this algorithm is still the important job to be researched in the future.

  14. Image Segmentation Via Color Clustering

    Kaveh Heidary

    2014-01-01

    This paper develops a computationally efficient process for segmentation of color images. The input image is partitioned into a set of output images in accordance to color characteristics of various image regions. The algorithm is based on random sampling of the input image and fuzzy clustering of the training data followed by crisp classification of the input image. The user prescribes the number of randomly selected pixels comprising the trainer set and the number of color classes character...

  15. Classification of open clusters by centroid method of taxonomical analysis

    Distributions of open clusters of the Galaxy in spaces with coordinates being mass, absolute magnitude, integrated colour index, diameter, metallicity, and age, are considered. Majority of clusters are shown to enter several taxons (classes) with narrow enough limits of these parameters. The classes form a linear sequence by age and two-dimensional sequence on colour - magnitude diagram. They are not isolated but transit into each other continuously. It possibly means an absence of significant gaps in cluster formation process. Bifurcation of age sequence of classes depending on mass and diameter values is found. This allows an evolutionary interpretation

  16. Magnetic properties of icosahedral MRu12 clusters

    The magnetic properties of icosahedral MRu12 clusters are studied using the discrete-variational local-spin-density-functional method, where M = V, Cr, Mn, Fe, Co, and Ni. The results show that all of the Ih MRu12 clusters, just like the case for the Ih Ru13 cluster, have double magnetic solutions. In contrast to the moment of 4 μB for the Ih Ru13 cluster, the total magnetic moments of the Ih MRu12 clusters, ranging from 1 μB to 20 μB, have been changed greatly by the substitution of the central Ru atom with M. Among them, the NiRu12 cluster has a giant moment of 20 μB. Furthermore, the NiRu12 cluster has nondegenerate ground state and could be expected to be remarkably stable. Therefore, for the purpose of enhancing the magnetic moment of the Ih Ru13 cluster, Ni is a promising candidate as a dopant. Finally, we predict that all the Ih MRu12 clusters except NiRu12 might belong to the class in which the magnetization of the cluster increases with temperature. (author)

  17. Clustering of drinker prototype characteristics: what characterizes the typical drinker?

    van Lettow, Britt; Vermunt, Jeroen K; de Vries, Hein; Burdorf, Alex; van Empelen, Pepijn

    2013-08-01

    Prototypes (social images) have been shown to influence behaviour, which is likely to depend on the type of image. Prototype evaluation is based on (un)desirable characteristics related to that image. By an elicitation procedure we examined which adjectives are attributed to specific drinker prototypes. In total 149 young Dutch adults (18-25 years of age) provided adjectives for five drinker prototypes: abstainer, moderate drinker, heavy drinker, tipsy, and drunk person. Twenty-three unique adjectives were found. Multilevel latent class cluster analysis revealed six adjective clusters, each with unique and minor overlapping adjectives: 'negative, excessive drinker,' 'moderate, responsible drinker,' 'funny tipsy drinker,' 'determined abstainer cluster,' 'uncontrolled excessive drinker,' and 'elated tipsy cluster.' In addition, four respondent classes were identified. Respondent classes showed differences in their focus on specific adjective clusters. Classes could be labelled 'focus-on-control class,' 'focus-on-hedonism class,' 'contrasting-extremes-prototypes class,' and 'focus-on-elation class.' Respondent classes differed in gender, educational level and drinking behaviour. The results underscore the importance to differentiate between various prototypes and in prototype adjectives among young adults: subgroup differences in prototype salience and relevance are possibly due to differences in adjective labelling. The results provide insights into explaining differences in drinking behaviour and could potentially be used to target and tailor interventions aimed at lowering alcohol consumption among young adults via prototype alteration. PMID:23848388

  18. Cluster automorphisms and compatibility of cluster variables

    Assem, Ibrahim; Schiffler, Ralf; Shramchenko, Vasilisa

    2013-01-01

    In this paper, we introduce a notion of unistructural cluster algebras, for which the set of cluster variables uniquely determines the clusters. We prove that cluster algebras of Dynkin type and cluster algebras of rank 2 are unistructural, then prove that if $\\mathcal{A}$ is unistructural or of Euclidean type, then $f: \\mathcal{A}\\to \\mathcal{A}$ is a cluster automorphism if and only if $f$ is an automorphism of the ambient field which restricts to a permutation of the cluster variables. In ...

  19. Horizontal Transfer and Death of a Fungal Secondary Metabolic Gene Cluster

    Campbell, Matthew A; Rokas, Antonis; Slot, Jason C.

    2012-01-01

    A cluster composed of four structural and two regulatory genes found in several species of the fungal genus Fusarium (class Sordariomycetes) is responsible for the production of the red pigment bikaverin. We discovered that the unrelated fungus Botrytis cinerea (class Leotiomycetes) contains a cluster of five genes that is highly similar in sequence and gene order to the Fusarium bikaverin cluster. Synteny conservation, nucleotide composition, and phylogenetic analyses of the cluster genes in...

  20. Globular Cluster Formation in the Virgo Cluster

    Moran, C Corbett; Lake, G

    2014-01-01

    Metal poor globular clusters (MPGCs) are a unique probe of the early universe, in particular the reionization era. Systems of globular clusters in galaxy clusters are particularly interesting as it is in the progenitors of galaxy clusters that the earliest reionizing sources first formed. Although the exact physical origin of globular clusters is still debated, it is generally admitted that globular clusters form in early, rare dark matter peaks (Moore et al. 2006; Boley et al. 2009). We provide a fully numerical analysis of the Virgo cluster globular cluster system by identifying the present day globular cluster system with exactly such early, rare dark matter peaks. A popular hypothesis is that that the observed truncation of blue metal poor globular cluster formation is due to reionization (Spitler et al. 2012; Boley et al. 2009; Brodie & Strader 2006); adopting this view, constraining the formation epoch of MPGCs provides a complementary constraint on the epoch of reionization. By analyzing both the l...

  1. Adaptive Evolutionary Clustering

    Xu, Kevin S.; Kliger, Mark; Hero III, Alfred O.

    2011-01-01

    In many practical applications of clustering, the objects to be clustered evolve over time, and a clustering result is desired at each time step. In such applications, evolutionary clustering typically outperforms traditional static clustering by producing clustering results that reflect long-term trends while being robust to short-term variations. Several evolutionary clustering algorithms have recently been proposed, often by adding a temporal smoothness penalty to the cost function of a st...

  2. Relational visual cluster validity

    Ding, Y.; Harrison, R F

    2007-01-01

    The assessment of cluster validity plays a very important role in cluster analysis. Most commonly used cluster validity methods are based on statistical hypothesis testing or finding the best clustering scheme by computing a number of different cluster validity indices. A number of visual methods of cluster validity have been produced to display directly the validity of clusters by mapping data into two- or three-dimensional space. However, these methods may lose too much information to corre...

  3. A pattern theorem for lattice clusters

    Madras, Neal

    1999-01-01

    We consider general classes of lattice clusters, including various kinds of animals and trees on different lattices. We prove that if a given local configuration ("pattern") of sites and bonds can occur in large clusters, then it occurs at least cN times in most clusters of size n, for some constant c>0. An analogous theorem for self-avoiding walks was proven in 1963 by Kesten. The results also apply to weighted sums, and in particular we can take a$sub n$ to be the probability that the perco...

  4. Text Clustering with String Kernels in R

    Karatzoglou, Alexandros; Feinerer , Ingo

    2006-01-01

    We present a package which provides a general framework, including tools and algorithms, for text mining in R using the S4 class system. Using this package and the kernlab R package we explore the use of kernel methods for clustering (e.g., kernel k-means and spectral clustering) on a set of text documents, using string kernels. We compare these methods to a more traditional clustering technique like k-means on a bag of word representation of the text and evaluate the viability of kernel-base...

  5. Double-partition Quantum Cluster Algebras

    Jakobsen, Hans Plesner; Zhang, Hechun

    2012-01-01

    A family of quantum cluster algebras is introduced and studied. In general, these algebras are new, but sub-classes have been studied previously by other authors. The algebras are indexed by double parti- tions or double flag varieties. Equivalently, they are indexed by broken lines L. By grouping...... together neighboring mutations into quantum line mutations we can mutate from the cluster algebra of one broken line to another. Compatible pairs can be written down. The algebras are equal to their upper cluster algebras. The variables of the quantum seeds are given by elements of the dual canonical basis....

  6. Issues Challenges and Tools of Clustering Algorithms

    Parul Agarwal

    2011-05-01

    Full Text Available Clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure. This paper has captured the problems that are faced in real when clustering algorithms are implemented .It also considers the most extensively used tools which are readily available and support functions which ease the programming. Once algorithms have been implemented, they also need to be tested for its validity. There exist several validation indexes for testing the performance and accuracy which have also been discussed here.

  7. Cluster formation in quantum critical systems

    The presence of magnetic clusters has been verified in both antiferromagnetic and ferromagnetic quantum critical systems. We review some of the strongest evidence for strongly doped quantum critical systems (Ce(Ru0.24Fe0.76)2Ge2) and we discuss the implications for the response of the system when cluster formation is combined with finite size effects. In particular, we discuss the change of universality class that is observed close to the order-disorder transition. We detail the conditions under which clustering effects will play a significant role also in the response of stoichiometric systems and their experimental signature.

  8. Loosely coupled class families

    Ernst, Erik

    2001-01-01

    Families of mutually dependent classes that may be accessed polymor- phically provide an advanced tool for separation of concerns, in that it enables client code to use a group of instances of related classes safely without depending on the exact classes involved. However, class families which are...... expressed using virtual classes seem to be very tightly coupled internally. While clients have achieved the freedom to dynamically use one or the other family, it seems that any given family contains a xed set of classes and we will need to create an entire family of its own just in order to replace one of...... the members with another class. This paper shows how to express class families in such a manner that the classes in these families can be used in many dierent combinations, still enabling family polymorphism and ensuring type safety....

  9. Comparative genomics of vertebrate Fox cluster loci

    Shimeld Sebastian M

    2006-10-01

    Full Text Available Abstract Background Vertebrate genomes contain numerous duplicate genes, many of which are organised into paralagous regions indicating duplication of linked groups of genes. Comparison of genomic organisation in different lineages can often allow the evolutionary history of such regions to be traced. A classic example of this is the Hox genes, where the presence of a single continuous Hox cluster in amphioxus and four vertebrate clusters has allowed the genomic evolution of this region to be established. Fox transcription factors of the C, F, L1 and Q1 classes are also organised in clusters in both amphioxus and humans. However in contrast to the Hox genes, only two clusters of paralogous Fox genes have so far been identified in the Human genome and the organisation in other vertebrates is unknown. Results To uncover the evolutionary history of the Fox clusters, we report on the comparative genomics of these loci. We demonstrate two further paralogous regions in the Human genome, and identify orthologous regions in mammalian, chicken, frog and teleost genomes, timing the duplications to before the separation of the actinopterygian and sarcopterygian lineages. An additional Fox class, FoxS, was also found to reside in this duplicated genomic region. Conclusion Comparison of loci identifies the pattern of gene duplication, loss and cluster break up through multiple lineages, and suggests FoxS1 is a likely remnant of Fox cluster duplication.

  10. NEO-FFI personality clusters in trichotillomania.

    Keuthen, Nancy J; Tung, Esther S; Tung, Matthew G; Curley, Erin E; Flessner, Christopher A

    2016-05-30

    The purpose of this study was to determine whether personality prototypes exist among hair pullers and if these groups differ in hair pulling (HP) characteristics, clinical correlates, and quality of life. 164 adult hair pullers completed the NEO-Five Factor Inventory (NEO-FFI; Costa and McCrae, 1992) and self-report measures of HP severity, HP style, affective state, and quality of life. A latent class cluster analysis using NEO-FFI scores was performed to separate participants into clusters. Bonferroni-corrected t-tests were used to compare clusters on HP, affective, and quality of life variables. Multiple regression was used to determine which variables significantly predicted quality of life. Two distinct personality prototypes were identified. Cluster 1 (n=96) had higher neuroticism and lower extraversion, agreeableness, and conscientiousness when compared to cluster 2 (n=68). No significant differences in demographics were reported for the two personality clusters. The clusters differed on extent of focused HP, severity of depression, anxiety, and stress, as well as quality of life. Those in cluster 1 endorsed greater depression, anxiety, and stress, and worse quality of life. Additionally, only depression and cluster membership (based on NEO scores) significantly predicted quality of life. PMID:27016621

  11. Partitional clustering algorithms

    2015-01-01

    This book summarizes the state-of-the-art in partitional clustering. Clustering, the unsupervised classification of patterns into groups, is one of the most important tasks in exploratory data analysis. Primary goals of clustering include gaining insight into, classifying, and compressing data. Clustering has a long and rich history that spans a variety of scientific disciplines including anthropology, biology, medicine, psychology, statistics, mathematics, engineering, and computer science. As a result, numerous clustering algorithms have been proposed since the early 1950s. Among these algorithms, partitional (nonhierarchical) ones have found many applications, especially in engineering and computer science. This book provides coverage of consensus clustering, constrained clustering, large scale and/or high dimensional clustering, cluster validity, cluster visualization, and applications of clustering. Examines clustering as it applies to large and/or high-dimensional data sets commonly encountered in reali...

  12. Cluster Evaluation of Density Based Subspace Clustering

    Sembiring, Rahmat Widia; Zain, Jasni Mohamad

    2010-01-01

    Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will occur in accordance with changes in density of each object neighbours. The neighbours of each object ...

  13. Clustering with Spectral Methods

    Gaertler, Marco

    2002-01-01

    Grouping and sorting are problems with a great tradition in the history of mankind. Clustering and cluster analysis is a small aspect in the wide spectrum. But these topics have applications in most scientific disciplines. Graph clustering is again a little fragment in the clustering area. Nevertheless it has the potential for new pioneering and innovative methods. One such method is the Markov Clustering presented by van Dongen in 'Graph Clustering by Flow Simulation'. We investigated the qu...

  14. Sparse Convex Clustering

    Wang, Binhuan; Zhang, Yilong; Sun, Wei; Fang, Yixin

    2016-01-01

    Convex clustering, a convex relaxation of k-means clustering and hierarchical clustering, has drawn recent attentions since it nicely addresses the instability issue of traditional nonconvex clustering methods. Although its computational and statistical properties have been recently studied, the performance of convex clustering has not yet been investigated in the high-dimensional clustering scenario, where the data contains a large number of features and many of them carry no information abo...

  15. A Virtual Class Calculus

    Ernst, Erik; Ostermann, Klaus; Cook, William Randall

    2006-01-01

    , statically typed model for virtual classes has been a long-standing open question. This paper presents a virtual class calculus, vc, that captures the essence of virtual classes in these full-fledged programming languages. The key contributions of the paper are a formalization of the dynamic and static...

  16. Entropic One-Class Classifiers.

    Livi, Lorenzo; Sadeghian, Alireza; Pedrycz, Witold

    2015-12-01

    The one-class classification problem is a well-known research endeavor in pattern recognition. The problem is also known under different names, such as outlier and novelty/anomaly detection. The core of the problem consists in modeling and recognizing patterns belonging only to a so-called target class. All other patterns are termed nontarget, and therefore, they should be recognized as such. In this paper, we propose a novel one-class classification system that is based on an interplay of different techniques. Primarily, we follow a dissimilarity representation-based approach; we embed the input data into the dissimilarity space (DS) by means of an appropriate parametric dissimilarity measure. This step allows us to process virtually any type of data. The dissimilarity vectors are then represented by weighted Euclidean graphs, which we use to determine the entropy of the data distribution in the DS and at the same time to derive effective decision regions that are modeled as clusters of vertices. Since the dissimilarity measure for the input data is parametric, we optimize its parameters by means of a global optimization scheme, which considers both mesoscopic and structural characteristics of the data represented through the graphs. The proposed one-class classifier is designed to provide both hard (Boolean) and soft decisions about the recognition of test patterns, allowing an accurate description of the classification process. We evaluate the performance of the system on different benchmarking data sets, containing either feature-based or structured patterns. Experimental results demonstrate the effectiveness of the proposed technique. PMID:25879977

  17. Gennclus: New Models for General Nonhierarchical Clustering Analysis.

    Desarbo, Wayne S.

    1982-01-01

    A general class of nonhierarchical clustering models and associated algorithms for fitting them are presented. These models generalize the Shepard-Arabie Additive clusters model. Two applications are given and extensions to three-way models, nonmetric analyses, and other model specifications are provided. (Author/JKS)

  18. FarMon: an extensible, efficient cluster monitoring system

    The authors present the design and implementation of FarMon- a flexible event monitoring system for computing cluster. Using several techniques including DCL (Dynamic Class Loading) technique, module publish/subscribe/unsubscribe protocol and directory service, the authors create a high efficient, high extensible and high portable cluster monitoring system

  19. A Survey of Popular R Packages for Cluster Analysis

    Flynt, Abby; Dean, Nema

    2016-01-01

    Cluster analysis is a set of statistical methods for discovering new group/class structure when exploring data sets. This article reviews the following popular libraries/commands in the R software language for applying different types of cluster analysis: from the stats library, the kmeans, and hclust functions; the mclust library; the poLCA…

  20. Learning predictive clustering rules

    Ženko, Bernard; Džeroski, Sašo; Struyf, Jan

    2005-01-01

    The two most commonly addressed data mining tasks are predictive modelling and clustering. Here we address the task of predictive clustering, which contains elements of both and generalizes them to some extent. We propose a novel approach to predictive clustering called predictive clustering rules, present an initial implementation and its preliminary experimental evaluation.

  1. Clustering of correlated networks

    Dorogovtsev, S. N.

    2003-01-01

    We obtain the clustering coefficient, the degree-dependent local clustering, and the mean clustering of networks with arbitrary correlations between the degrees of the nearest-neighbor vertices. The resulting formulas allow one to determine the nature of the clustering of a network.

  2. Structures of Mn clusters

    Tina M Briere; Marcel H F Sluiter; Vijay Kumar; Yoshiyuki Kawazoe

    2003-01-01

    The geometries of several Mn clusters in the size range Mn13–Mn23 are studied via the generalized gradient approximation to density functional theory. For the 13- and 19-atom clusters, the icosahedral structures are found to be most stable, while for the 15-atom cluster, the bcc structure is more favoured. The clusters show ferrimagnetic spin configurations.

  3. Foodservice Occupations Cluster Guide.

    Oregon State Dept. of Education, Salem.

    Intended to assist vocational teachers in developing and implementing a cluster program in food service occupations, this guide contains sections on cluster organization and implementation and instructional emphasis areas. The cluster organization and implementation section covers goal-based planning and includes a proposed cluster curriculum, a…

  4. On Comparison of Clustering Methods for Pharmacoepidemiological Data.

    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. PMID:24905478

  5. Relevant Subspace Clustering

    Müller, Emmanuel; Assent, Ira; Günnemann, Stephan; Krieger, Ralph; Seidl, Thomas

    Subspace clustering aims at detecting clusters in any subspace projection of a high dimensional space. As the number of possible subspace projections is exponential in the number of dimensions, the result is often tremendously large. Recent approaches fail to reduce results to relevant subspace...... clusters. Their results are typically highly redundant, i.e. many clusters are detected multiple times in several projections. In this work, we propose a novel model for relevant subspace clustering (RESCU). We present a global optimization which detects the most interesting non-redundant subspace clusters...... achieves top clustering quality while competing approaches show greatly varying performance....

  6. Tilting theory and cluster algebras

    Reiten, Idun

    2010-01-01

    We give an introduction to the theory of cluster categories and cluster tilted algebras. We include some background on the theory of cluster algebras, and discuss the interplay with cluster categories and cluster tilted algebras.

  7. Parallel Local Graph Clustering

    Shun, Julian; Roosta-Khorasani, Farbod; Fountoulakis, Kimon; Mahoney, Michael W.

    2016-01-01

    Graph clustering has many important applications in computing, but due to growing sizes of graph, even traditionally fast clustering methods such as spectral partitioning can be computationally expensive for real-world graphs of interest. Motivated partly by this, so-called local algorithms for graph clustering have received significant interest due to the fact that they can find good clusters in a graph with work proportional to the size of the cluster rather than that of the entire graph. T...

  8. Cluster ion beam facilities

    A brief state-of-the-art review in the field of cluster-surface interactions is presented. Ionised cluster beams could become a powerful and versatile tool for the modification and processing of surfaces as an alternative to ion implantation and ion assisted deposition. The main effects of cluster-surface collisions and possible applications of cluster ion beams are discussed. The outlooks of the Cluster Implantation and Deposition Apparatus (CIDA) being developed in Guteborg University are shown

  9. Graded cluster algebras

    Grabowski, Jan

    2015-01-01

    In the cluster algebra literature, the notion of a graded cluster algebra has been implicit since the origin of the subject. In this work, we wish to bring this aspect of cluster algebra theory to the foreground and promote its study. We transfer a definition of Gekhtman, Shapiro and Vainshtein to the algebraic setting, yielding the notion of a multi-graded cluster algebra. We then study gradings for finite type cluster algebras without coefficients, giving a full classification. Translating ...

  10. A Density Based Dynamic Data Clustering Algorithm based on Incremental Dataset

    K. R.S. Kumar

    2012-01-01

    Full Text Available Problem statement: Clustering and visualizing high-dimensional dynamic data is a challenging problem. Most of the existing clustering algorithms are based on the static statistical relationship among data. Dynamic clustering is a mechanism to adopt and discover clusters in real time environments. There are many applications such as incremental data mining in data warehousing applications, sensor network, which relies on dynamic data clustering algorithms. Approach: In this work, we present a density based dynamic data clustering algorithm for clustering incremental dataset and compare its performance with full run of normal DBSCAN, Chameleon on the dynamic dataset. Most of the clustering algorithms perform well and will give ideal performance with good accuracy measured with clustering accuracy, which is calculated using the original class labels and the calculated class labels. However, if we measure the performance with a cluster validation metric, then it will give another kind of result. Results: This study addresses the problems of clustering a dynamic dataset in which the data set is increasing in size over time by adding more and more data. So to evaluate the performance of the algorithms, we used Generalized Dunn Index (GDI, Davies-Bouldin index (DB as the cluster validation metric and as well as time taken for clustering. Conclusion: In this study, we have successfully implemented and evaluated the proposed density based dynamic clustering algorithm. The performance of the algorithm was compared with Chameleon and DBSCAN clustering algorithms. The proposed algorithm performed significantly well in terms of clustering accuracy as well as speed.

  11. Cool Core Clusters from Cosmological Simulations

    Rasia, E; Murante, G; Planelles, S; Beck, A M; Biffi, V; Ragone-Figueroa, C; Granato, G L; Steinborn, L K; Dolag, K

    2015-01-01

    We present results obtained from a set of cosmological hydrodynamic simulations of galaxy clusters, aimed at comparing predictions with observational data on the diversity between cool-core and non-cool-core clusters. Our simulations include the effects of stellar and AGN feedback and are based on an improved version of the Smoothed-Particle-Hydrodynamics code GADGET-3, which ameliorates gas mixing and better captures gas-dynamical instabilities by including a suitable artificial thermal diffusion. In this Letter, we focus our analysis on the entropy profiles, our primary diagnostic to classify the degree of cool-coreness of clusters, and on the iron profiles. In keeping with observations, our simulated clusters display a variety of behaviors in entropy profiles: they range from steadily decreasing profiles at small radii, characteristic of cool-core systems, to nearly flat core isentropic profiles, characteristic of non cool-core systems. Using observational criteria to distinguish between the two classes of...

  12. UCD Candidates in the Hydra Cluster

    Wehner, Elizabeth

    2007-01-01

    NGC 3311, the giant cD galaxy in the Hydra cluster (A1060), has one of the largest globular cluster systems known. We describe new Gemini GMOS (g',i') photometry of the NGC 3311 field which reveals that the red, metal-rich side of its globular cluster population extends smoothly upward into the mass range associated with the new class of Ultra-Compact Dwarfs (UCDs). We identify 29 UCD candidates with estimated masses > 6x10^6 solar masses and discuss their characteristics. This UCD-like sequence is the most well defined one yet seen, and reinforces current ideas that the high-mass end of the globular cluster sequence merges continuously into the UCD sequence, which connects in turn to the E galaxy structural sequence.

  13. Semantic Analysis of Virtual Classes and Nested Classes

    Madsen, Ole Lehrmann

    1999-01-01

    Virtual classes and nested classes are distinguishing features of BETA. Nested classes originated from Simula, but until recently they have not been part of main stream object- oriented languages. C++ has a restricted form of nested classes and they were included in Java 1.1. Virtual classes is the...... classes and parameterized classes have been made. Although virtual classes and nested classes have been used in BETA for more than a decade, their implementation has not been published. The purpose of this paper is to contribute to the understanding of virtual classes and nested classes by presenting the...

  14. Class network routing

    Bhanot, Gyan; Blumrich, Matthias A.; Chen, Dong; Coteus, Paul W.; Gara, Alan G.; Giampapa, Mark E.; Heidelberger, Philip; Steinmacher-Burow, Burkhard D.; Takken, Todd E.; Vranas, Pavlos M.

    2009-09-08

    Class network routing is implemented in a network such as a computer network comprising a plurality of parallel compute processors at nodes thereof. Class network routing allows a compute processor to broadcast a message to a range (one or more) of other compute processors in the computer network, such as processors in a column or a row. Normally this type of operation requires a separate message to be sent to each processor. With class network routing pursuant to the invention, a single message is sufficient, which generally reduces the total number of messages in the network as well as the latency to do a broadcast. Class network routing is also applied to dense matrix inversion algorithms on distributed memory parallel supercomputers with hardware class function (multicast) capability. This is achieved by exploiting the fact that the communication patterns of dense matrix inversion can be served by hardware class functions, which results in faster execution times.

  15. Subpopulation Discovery in Epidemiological Data with Subspace Clustering

    Niemann Uli

    2014-12-01

    Full Text Available A prerequisite of personalized medicine is the identification of groups of people who share specific risk factors towards an outcome. We investigate the potential of subspace clustering for finding such groups in epidemiological data. We propose a workflow that encompasses clusterability assessment before cluster discovery and quality assessment after learning the clusters. Epidemiological usually do not have a ground truth for the verification of clusters found in subspaces. Hence, we introduce quality assessment through juxtaposition of the learned models to “models-of-randomness”, i.e. models that do not reflect a true cluster structure. On the basis of this workflow, we select subspace clustering methods, compare and discuss their performance. We use a dataset with hepatic steatosis as outcome, but our findings apply on arbitrary epidemiological cohort data that have tenths of variables and exhibit class skew.

  16. Fostering a Middle Class

    YAO BIN

    2011-01-01

    Though there is no official definition of "middle class" in China,the tag has become one few Chinese people believe they deserve anyway.In early August,the Chinese Academy of Social Sciences released a report on China's urban development,saying China had a middle-class population of 230 million in 2009,or 37 percent of its urban residents.It also forecast half of city dwellers in China would be part of the middle class by 2023.

  17. Cluster Evaluation of Density Based Subspace Clustering

    Sembiring, Rahmat Widia

    2010-01-01

    Clustering real world data often faced with curse of dimensionality, where real world data often consist of many dimensions. Multidimensional data clustering evaluation can be done through a density-based approach. Density approaches based on the paradigm introduced by DBSCAN clustering. In this approach, density of each object neighbours with MinPoints will be calculated. Cluster change will occur in accordance with changes in density of each object neighbours. The neighbours of each object typically determined using a distance function, for example the Euclidean distance. In this paper SUBCLU, FIRES and INSCY methods will be applied to clustering 6x1595 dimension synthetic datasets. IO Entropy, F1 Measure, coverage, accurate and time consumption used as evaluation performance parameters. Evaluation results showed SUBCLU method requires considerable time to process subspace clustering; however, its value coverage is better. Meanwhile INSCY method is better for accuracy comparing with two other methods, altho...

  18. Clustering Algorithm Based on Crowding Niche%小生境排挤聚类算法

    业宁; 董逸生

    2003-01-01

    A new clustering algorithm is proposed in this paper, which is based on crowding niche. Homogeneityspontaneous to withstands heterogeneity when organisms are evolving. Contemporary, Individual in same class com-pete each other to strive for limited resource. Individual that has bad fitness will be eliminated. We propose a cluster-ing algorithm based on this idea. Experiment evaluation has proved its efficiency.

  19. Coupled Two-Way Clustering Analysis of Breast Cancer and Colon Cancer Gene Expression Data

    Getz, G; Kela, I; Domany, E; Notterman, D A; Getz, Gad; Gal, Hilah; Kela, Itai; Domany, Eytan; Notterman, Dan A.

    2003-01-01

    We present and review Coupled Two Way Clustering, a method designed to mine gene expression data. The method identifies submatrices of the total expression matrix, whose clustering analysis reveals partitions of samples (and genes) into biologically relevant classes. We demonstrate, on data from colon and breast cancer, that we are able to identify partitions that elude standard clustering analysis.

  20. New laser classes

    By an up-dated international standard (IEC 60825-1 + Amendment 2) on laser safety some new laser classes are introduced. The new set of laser classes consists of 1, 1M, 2, 2M, 3R, 3B, and 4. This is a result of intense discussions in the committee and was laid down in 2000, slightly adjusted 2001. The previous classes 1, 2, 3A, 3B, and 4, established since more than 25 years, are partly abandoned. This paper compares the new classes to the old ones. (orig.)

  1. Class, Culture and Politics

    Harrits, Gitte Sommer

    2013-01-01

    , discussions within political sociology have not yet utilized the merits of a multidimensional conception of class. In light of this, the article suggests a comprehensive Bourdieusian framework for class analysis, integrating culture as both a structural phenomenon co-constitutive of class and as symbolic...... practice. Further, the article explores this theoretical framework in a multiple correspondence analysis of a Danish survey, demonstrating how class and political practices are indeed homologous. However, the analysis also points at several elements of field autonomy, and the concluding discussion...

  2. Media Clusters and Media Cluster Policies

    Karlsson, Charlie; Picard, Robert

    2011-01-01

    Large media clusters have emerged in a limited number of large cities, characterizing the geographical concentration of the global media industry. This paper explores the reasons behind the localization patterns of media industries, the effect of the rapid advancement of Information and Communication Technologies (ICT) on media clusters and the role of media cluster policies. One might draw the conclusion that with the developments of the ICT sector and the fact that there are no raw material...

  3. Cluster selection in divisive clustering algorithms

    Savaresi, Sergio,; Boley, Daniel L.; Bittanti, Sergio; Gazzaniga, Giovanna

    2002-01-01

    This paper deals with the problem of clustering a data-set. In particular, the bisecting divisive approach is here considered. This approach can be naturally divided into two sub-problems: the problem of choosing which cluster must be divided, and the problem of splitting the selected cluster. The focus here is on the first problem. The contribution of this work is to propose a new technique for the selection of the cluster to split. This technique is based upon the shape of...

  4. All about RIKEN Integrated Cluster of Clusters (RICC

    Maho Nakata

    2012-07-01

    Full Text Available

    This is an introduction to the RIKEN's supercomputer RIKEN Integrated Cluster of Clusters (RICC, that has been in operation since August 2009. The basic concept of the RICC is to "provide an environment with high power computational resources to facilitate research and development for RIKEN's researchers". Based on this concept, we have been operating the RICC system as a (i data analysis environment for experimental researchers, (ii development environment targeting the next-generation supercomputer; i.e., the "K" computer, and (iii GPU (graphics processing unit computers for exploring challenges in developing a future computer environment. The total performance of RICC is 97.94 TFlops, ranking it as the 125th on the Top500 list in Nov. 2011. We prepared four job class accounts, based on the researchers' proposals prior to evaluation by our Review Committee. We also provided backup services to RIKEN's researchers, such as conducting RICC training classes, software installation services, and speed up and visualization support. To encourage affirmative participation and proactive initiation, all the services were free of charge; however, access to RICC was limited to researchers and collaborators of RIKEN. As a result, RICC has been able to maintain a high activity ratio (> 90% since the beginning of its operation.

  5. Galaxy Luminosity Functions in WINGS clusters

    Moretti, A; Poggianti, B M; Fasano, G; Varela, J; D'Onofrio, M; Vulcani, B; Cava, A; Fritz, J; Couch, W J; Moles, M; Kjærgaard, P

    2015-01-01

    Using V band photometry of the WINGS survey, we derive galaxy luminosity functions (LF) in nearby clusters. This sample is complete down to Mv=-15.15, and it is homogeneous, thus allowing the study of an unbiased sample of clusters with different characteristics. We constructed the photometric LF for 72 out of the original 76 WINGS clusters, excluding only those without a velocity dispersion estimate. For each cluster we obtained the LF for galaxies in a region of radius=0.5 x r200, and fitted them with single and double Schechter's functions. We also derive the composite LF for the entire sample, and those pertaining to different morphological classes. Finally we derive the spectroscopic cumulative LF for 2009 galaxies that are cluster members. The double Schechter fit parameters are neither correlated with the cluster velocity dispersion, nor with the X-ray luminosity. Our median values of the Schechter's fit slope are, on average, in agreement with measurements of nearby clusters, but are less steep that t...

  6. Young massive star clusters

    Zwart, Simon Portegies; Gieles, Mark

    2010-01-01

    Young massive clusters are dense aggregates of young stars that form the fundamental building blocks of galaxies. Several examples exist in the Milky Way Galaxy and the Local Group, but they are particularly abundant in starburst and interacting galaxies. The few young massive clusters that are close enough to resolve are of prime interest for studying the stellar mass function and the ecological interplay between stellar evolution and stellar dynamics. The distant unresolved clusters may be effectively used to study the star-cluster mass function, and they provide excellent constraints on the formation mechanisms of young cluster populations. Young massive clusters are expected to be the nurseries for many unusual objects, including a wide range of exotic stars and binaries. So far only a few such objects have been found in young massive clusters, although their older cousins, the globular clusters, are unusually rich in stellar exotica. In this review we focus on star clusters younger than $\\sim100$\\,Myr, m...

  7. Determining the Optimal Number of Clusters with the Clustergram

    Fluegemann, Joseph K.; Davies, Misty D.; Aguirre, Nathan D.

    2011-01-01

    Cluster analysis aids research in many different fields, from business to biology to aerospace. It consists of using statistical techniques to group objects in large sets of data into meaningful classes. However, this process of ordering data points presents much uncertainty because it involves several steps, many of which are subject to researcher judgment as well as inconsistencies depending on the specific data type and research goals. These steps include the method used to cluster the data, the variables on which the cluster analysis will be operating, the number of resulting clusters, and parts of the interpretation process. In most cases, the number of clusters must be guessed or estimated before employing the clustering method. Many remedies have been proposed, but none is unassailable and certainly not for all data types. Thus, the aim of current research for better techniques of determining the number of clusters is generally confined to demonstrating that the new technique excels other methods in performance for several disparate data types. Our research makes use of a new cluster-number-determination technique based on the clustergram: a graph that shows how the number of objects in the cluster and the cluster mean (the ordinate) change with the number of clusters (the abscissa). We use the features of the clustergram to make the best determination of the cluster-number.

  8. Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning

    Ntampaka, Michelle; Trac, Hy; Sutherland, Dougal; Fromenteau, Sebastien; Poczos, Barnabas; Schneider, Jeff

    2016-01-01

    Galaxy clusters are a rich source of information for examining fundamental astrophysical processes and cosmological parameters, however, employing clusters as cosmological probes requires accurate mass measurements derived from cluster observables. We study dynamical mass measurements of galaxy clusters contaminated by interlopers, and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create a mock catalog from Multidark's publicly-available N-body MDPL1 simulation where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power law scaling relation to infer cluster mass from galaxy line of sight (LOS) velocity dispersion. The presence of interlopers in the catalog produces a wide, flat fractional mass error distribution, with width = 2.13. We employ the Support Distribution Machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to distributions of galaxy observables such as LOS velocity and projected distance from the cluster center, SDM yields better than a factor-of-two improvement (width = 0.67). Remarkably, SDM applied to contaminated clusters is better able to recover masses than even a scaling relation approach applied to uncontaminated clusters. We show that the SDM method more accurately reproduces the cluster mass function, making it a valuable tool for employing cluster observations to evaluate cosmological models.

  9. Teaching Large Evening Classes

    Wambuguh, Oscar

    2008-01-01

    High enrollments, conflicting student work schedules, and the sheer convenience of once-a-week classes are pushing many colleges to schedule evening courses. Held from 6 to 9 pm or 7 to 10 pm, these classes are typically packed, sometimes with more than 150 students in a large lecture theater. How can faculty effectively teach, control, or even…

  10. Class in disguise

    Faber, Stine Thidemann; Prieur, Annick

    This paper asks how class can have importance in one of the worlds’ most equal societies: Denmark. The answer is that class here appears in disguised forms. The field under study is a city, Aalborg, in the midst of transition from a stronghold of industrialism to a post industrial economy...

  11. Teaching Social Class

    Tablante, Courtney B.; Fiske, Susan T.

    2015-01-01

    Discussing socioeconomic status in college classes can be challenging. Both teachers and students feel uncomfortable, yet social class matters more than ever. This is especially true, given increased income inequality in the United States and indications that higher education does not reduce this inequality as much as many people hope. Resources…

  12. DEFINING THE MIDDLE CLASS

    2011-01-01

    Classifying the middle class remains controversial despite its alleged growth China’s cities housed more than 230 million middle-class residents in 2009 or 37 percent of the urban population,according to the 2011 Blue Book of Cities in China released on August 3.

  13. What Makes Clusters Decline?

    Østergaard, Christian Richter; Park, Eun Kyung

    2015-01-01

    Most studies on regional clusters focus on identifying factors and processes that make clusters grow. However, sometimes technologies and market conditions suddenly shift, and clusters decline. This paper analyses the process of decline of the wireless communication cluster in Denmark. The...... longitudinal study on the high-tech cluster reveals that technological lock-in and exit of key firms have contributed to decline. Entrepreneurship has a positive effect on the cluster’s adaptive capabilities, while multinational companies have contradicting effects by bringing in new resources to the cluster...

  14. Morphology of a class of kinetic-growth models

    We study a class of local probabilistic growth processes that includes the kinetic-growth algorithm for generating percolation clusters. The shapes of the growing clusters are controlled by p, the probability of growth. For p > p/sub c/, the shapes are scale invariant with time and show interesting morphological features including both smoothly curved sections and straight facets. The facets are shown to be related to the problem of directed percolation and disappear below the directed-percolation threshold. A simple random-walk model for computing the shapes of our clusters is described

  15. Analysis of Various Clustering Algorithms

    Asst Prof. Sunila Godara,; Ms. Amita Verma,

    2013-01-01

    Data clustering is a process of putting similar data into groups. A clustering algorithm partitions a data set into several groups such that the similarity within a group is larger than among groups. This paper reviews four types of clustering techniques- k-Means Clustering, Farther first clustering, Density Based Clustering, Filtered clusterer. These clustering techniques are implemented and analyzed using a clustering tool WEKA. Performance of the 4 techniques are presented and compared.

  16. Monopole clusters in Abelian projected gauge theories

    Hart, A.; Teper, M.

    1997-01-01

    We show that the monopole currents which one obtains in the maximally Abelian gauge of SU(2) fall into two quite distinct classes (when the volume is large enough). In each field configuration there is precisely one cluster that permeates the whole lattice volume. It has a current density and a magnetic screening mass that scale and it produces the whole of the string tension. The remaining clusters have a number density that follows an approximate power law proportional to the inverse cube o...

  17. Niching method using clustering crowding

    GUO Guan-qi; GUI Wei-hua; WU Min; YU Shou-yi

    2005-01-01

    This study analyzes drift phenomena of deterministic crowding and probabilistic crowding by using equivalence class model and expectation proportion equations. It is proved that the replacement errors of deterministic crowding cause the population converging to a single individual, thus resulting in premature stagnation or losing optional optima. And probabilistic crowding can maintain equilibrium multiple subpopulations as the population size is adequate large. An improved niching method using clustering crowding is proposed. By analyzing topology of fitness landscape using hill valley function and extending the search space for similarity analysis, clustering crowding determines the locality of search space more accurately, thus greatly decreasing replacement errors of crowding. The integration of deterministic and probabilistic replacement increases the capacity of both parallel local hill climbing and maintaining multiple subpopulations. The experimental results optimizing various multimodal functions show that,the performances of clustering crowding, such as the number of effective peaks maintained, average peak ratio and global optimum ratio are uniformly superior to those of the evolutionary algorithms using fitness sharing, simple deterministic crowding and probabilistic crowding.

  18. Star clusters and associations

    All 33 papers presented at the symposium were inputted to INIS. They dealt with open clusters, globular clusters, stellar associations and moving groups, and local kinematics and galactic structures. (E.S.)

  19. Melting of clusters

    Haberland, H. [Freiburg Univ., Facultat fur Physik (Germany)

    2001-07-01

    An experiment is described which allows to measure the caloric curve of size selected sodium cluster ions. This allows to determine rather easily the melting temperatures, and latent heats in the size range between 55 and 340 atoms per cluster. A more detailed analysis is necessary to show that the cluster Na{sub 147}{sup +} has a negative microcanonical heat capacity, and how to determine the entropy of the cluster from the data. (authors)

  20. An Efficient Enhanced Clustering Algorithm of Information System For Law Enforcement

    Dr. A. Malathi; Dr. P. Rajarajeswari

    2014-01-01

    Clustering is a popular data mining techniques which is intended to help the user discover and understand the structure or grouping of the data in the set according to a certain similarity measure and predict future structure or group respectively. Clustering is the process of class discovery, where the objects are grouped into clusters. In this paper Enhanced K-Means and Enhanced DBSCAN algorithms are designed and used for the clustering crime data in the proposed crime analysis tool. Anothe...

  1. Using Curvature and Markov Clustering in Graphs for Lexical Acquisition and Word Sense Discrimination

    Dorow, Beate; Widdows, Dominic; Ling, Katarina; Eckmann, Jean-Pierre; Sergi, Danilo; Moses, Elisha

    2004-01-01

    We introduce two different approaches for clustering semantically similar words. We accommodate ambiguity by allowing a word to belong to several clusters. Both methods use a graph-theoretic representation of words and their paradigmatic relationships. The first approach is based on the concept of curvature and divides the word graph into classes of similar words by removing words of low curvature which connect several dispersed clusters. The second method, instead of clustering the nodes, cl...

  2. Pattern Formation and a Clustering Transition in Power-Law Sequential Adsorption

    Biham, Ofer; Malcai, Ofer; Lidar, Daniel A.; Avnir, David

    1999-01-01

    A new model that describes adsorption and clustering of particles on a surface is introduced. A {\\it clustering} transition is found which separates between a phase of weakly correlated particle distributions and a phase of strongly correlated distributions in which the particles form localized fractal clusters. The order parameter of the transition is identified and the fractal nature of both phases is examined. The model is relevant to a large class of clustering phenomena such as aggregati...

  3. Cluster beam sources. Part 1. Methods of cluster beams generation

    A.Ju. Karpenko

    2012-10-01

    Full Text Available The short review on cluster beams generation is proposed. The basic types of cluster sources are considered and the processes leading to cluster formation are analyzed. The parameters, that affects the work of cluster sources are presented.

  4. Cluster beam sources. Part 1. Methods of cluster beams generation

    A.Ju. Karpenko; V.A. Baturin

    2012-01-01

    The short review on cluster beams generation is proposed. The basic types of cluster sources are considered and the processes leading to cluster formation are analyzed. The parameters, that affects the work of cluster sources are presented.

  5. Multireference Coupled Cluster Ansatz

    Jeziorski, Bogumil

    2010-01-01

    Abstract The origin of the multireference coupled cluster Ansatz for the wave function and the wave operator, discovered in Quantum Theory Project in 1981, is presented from the historical perspective. Various methods of obtaining the cluster amplitudes - both state universal and state selective are critically reviewed and further prospects of using the multireference coupled cluster Ansatz in electronic structure theory are briefly discussed.

  6. Quantum Annealing for Clustering

    Kurihara, Kenichi; Tanaka, Shu; Miyashita, Seiji

    2014-01-01

    This paper studies quantum annealing (QA) for clustering, which can be seen as an extension of simulated annealing (SA). We derive a QA algorithm for clustering and propose an annealing schedule, which is crucial in practice. Experiments show the proposed QA algorithm finds better clustering assignments than SA. Furthermore, QA is as easy as SA to implement.

  7. The Durban Auto Cluster

    Lorentzen, Jochen; Robbins, Glen; Barnes, Justin

    2004-01-01

    The paper describes the formation of the Durban Auto Cluster in the context of trade liberalization. It argues that the improvement of operational competitiveness of firms in the cluster is prominently due to joint action. It tests this proposition by comparing the gains from cluster activities i...

  8. Relational aspects of clusters

    Gjerding, Allan Næs

    The present paper is the first preliminary account of a project being planned for 2013, focussing on the development of the biomedico cluster in North Denmark. The project focusses on the relational capabilities of the cluster in terms of a number of organizational roles which are argued to be...... necessary for the development and growth of the upcoming cluster in question....

  9. Minimalist's linux cluster

    Using barebone PC components and NIC's, we construct a linux cluster which has 2-dimensional mesh structure. This cluster has smaller footprint, is less expensive, and use less power compared to conventional linux cluster. Here, we report our experience in building such a machine and discuss our current lattice project on the machine

  10. Social Class Dialogues and the Fostering of Class Consciousness

    Madden, Meredith

    2015-01-01

    How do critical pedagogies promote undergraduate students' awareness of social class, social class identity, and social class inequalities in education? How do undergraduate students experience class consciousness-raising in the intergroup dialogue classroom? This qualitative study explores undergraduate students' class consciousness-raising in an…

  11. Cluster Physics with Merging Galaxy Clusters

    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.

  12. Clustering high dimensional data using subspace and projected clustering algorithms

    Rahmat Widia Sembiring; Jasni Mohamad Zain; Abdullah Embong

    2010-01-01

    Problem statement: Clustering has a number of techniques that have been developed in statistics, pattern recognition, data mining, and other fields. Subspace clustering enumerates clusters of objects in all subspaces of a dataset. It tends to produce many over lapping clusters. Approach: Subspace clustering and projected clustering are research areas for clustering in high dimensional spaces. In this research we experiment three clustering oriented algorithms, PROCLUS, P3C and STATPC. Results...

  13. Teaching Heterogeneous Classes.

    Millrood, Radislav

    2002-01-01

    Discusses an approach to teaching heterogeneous English-as-a-Second/Foreign-Language classes. Draws on classroom research data to describe the features of a success-building lesson context. (Author/VWL)

  14. IELP Class Observation

    陈了了

    2010-01-01

    @@ As an exchange student majoring in English, I am curious about how English is taught to international students here in America. Therefore, I observed an IELP (Intensive English Learning Program) class in Central Connecticut State University where I study.

  15. PSYCH 515 Complete Class

    admin

    2015-01-01

      PSYCH 515 Advanced Abnormal Psychology To purchase this material click on below link http://www.assignmentcloud.com/PSYCH-515/PSYCH-515-Complete-Class-Guide For more details www.assignmentcloud.com

  16. Raradox of class description

    吕光

    2004-01-01

    We have a more active class atmophere, but more passive self-study situations. We are too talktive when we should bury ourselves in books, but too less efficient when we spend too much time. We complain teachers

  17. Equilibrium and flow of cluster-forming complex fluids

    Full text: In this talk, I will present an overview of the unusual properties of a novel class of systems in soft matter physics, in which cluster formation takes place in the complete absence of attractions. After formulating a mathematical criterion as a necessary and sufficient condition for cluster formation, I will discuss the unusual structural, dynamical and phononic properties of cluster solids in equilibrium, showing, among others, that these are diffusive, that they provide for a realization of the Einstein model of solids and that at low temperatures cluster solids posses infinitely many isostructural critical points. Under shear flow, cluster solids organize in forms resembling the Abrikosov lattice of superconductors and they show a pressure flow behavior typical of colloidal glasses. Finally, I will demonstrate the construction of realistic microscopic models that allow for the formation of cluster crystals in the computer, opening the way to their experimental realization. (author)

  18. A Novel Clustering Algorithm Inspired by Membrane Computing

    Hong Peng

    2015-01-01

    Full Text Available P systems are a class of distributed parallel computing models; this paper presents a novel clustering algorithm, which is inspired from mechanism of a tissue-like P system with a loop structure of cells, called membrane clustering algorithm. The objects of the cells express the candidate centers of clusters and are evolved by the evolution rules. Based on the loop membrane structure, the communication rules realize a local neighborhood topology, which helps the coevolution of the objects and improves the diversity of objects in the system. The tissue-like P system can effectively search for the optimal partitioning with the help of its parallel computing advantage. The proposed clustering algorithm is evaluated on four artificial data sets and six real-life data sets. Experimental results show that the proposed clustering algorithm is superior or competitive to k-means algorithm and several evolutionary clustering algorithms recently reported in the literature.

  19. Nordic Walking Classes

    Fitness Club

    2015-01-01

    Four classes of one hour each are held on Tuesdays. RDV barracks parking at Entrance A, 10 minutes before class time. Spring Course 2015: 05.05/12.05/19.05/26.05 Prices 40 CHF per session + 10 CHF club membership 5 CHF/hour pole rental Check out our schedule and enroll at: https://espace.cern.ch/club-fitness/Lists/Nordic%20Walking/NewForm.aspx? Hope to see you among us! fitness.club@cern.ch

  20. Embodying class and gender

    Geers, Alexie

    2015-01-01

    In March 1937, when the first issue of Marie-Claire was published, the images of the female body it presented to its female readers from working-class backgrounds contrasted sharply with those featured in previous magazines. The female bodies are dressed and groomed to seduce and replace the hieratic bodies that presented fashions synonymous with membership in the upper classes. The present essay examines this shift and shows that the visual repertoire employed is borrowed from that of the fe...

  1. Generalized Fourier transforms classes

    Berntsen, Svend; Møller, Steen

    2002-01-01

    The Fourier class of integral transforms with kernels $B(\\omega r)$ has by definition inverse transforms with kernel $B(-\\omega r)$. The space of such transforms is explicitly constructed. A slightly more general class of generalized Fourier transforms are introduced. From the general theory foll...... follows that integral transform with kernels which are products of a Bessel and a Hankel function or which is of a certain general hypergeometric type have inverse transforms of the same structure....

  2. Polynuclear technetium halide clusters

    Development of chemistry of polynuclear technetium halide clusters in works devoted to synthesis, structure and investigation of their chemical and physical properties is considered. The role of academician V.I. Spitsyn as an initiator of investigation of polynuclear technetium halide clusters in the Institute of Physical Chemistry of Academy of Science of USSR is noted. Reactions and stability of cluster halides, their molecular and electronic structures are analyzed. Prospects of development of polynuclear technetium halide clusters chemistry as a direction being on the junction of cluster chemistry and theory of metal-metal multiple bonds are appreciated

  3. Cluster analysis for applications

    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

  4. Chaotic map clustering algorithm for EEG analysis

    Bellotti, R.; De Carlo, F.; Stramaglia, S.

    2004-03-01

    The non-parametric chaotic map clustering algorithm has been applied to the analysis of electroencephalographic signals, in order to recognize the Huntington's disease, one of the most dangerous pathologies of the central nervous system. The performance of the method has been compared with those obtained through parametric algorithms, as K-means and deterministic annealing, and supervised multi-layer perceptron. While supervised neural networks need a training phase, performed by means of data tagged by the genetic test, and the parametric methods require a prior choice of the number of classes to find, the chaotic map clustering gives a natural evidence of the pathological class, without any training or supervision, thus providing a new efficient methodology for the recognition of patterns affected by the Huntington's disease.

  5. Survey on Text Document Clustering

    M.Thangamani; Dr.P.Thangaraj

    2010-01-01

    Document clustering is also referred as text clustering, and its concept is merely equal to data clustering. It is hardly difficult to find the selective information from an ‘N’number of series information, so that document clustering came into picture. Basically cluster means a group of similar data, document clustering means segregating the data into different groups of similar data. Clustering can be of mathematical, statistical or numerical domain. Clustering is a fundamental data analysi...

  6. Unconventional methods for clustering

    Kotyrba, Martin

    2016-06-01

    Cluster analysis or clustering is a task of grouping a set of objects in such a way that objects in the same group (called a cluster) are more similar (in some sense or another) to each other than to those in other groups (clusters). It is the main task of exploratory data mining and a common technique for statistical data analysis used in many fields, including machine learning, pattern recognition, image analysis, information retrieval, and bioinformatics. The topic of this paper is one of the modern methods of clustering namely SOM (Self Organising Map). The paper describes the theory needed to understand the principle of clustering and descriptions of algorithm used with clustering in our experiments.

  7. Clusters in nuclei

    Beck, Christian

    Following the pioneering discovery of alpha clustering and of molecular resonances, the field of nuclear clustering is today one of those domains of heavy-ion nuclear physics that faces the greatest challenges, yet also contains the greatest opportunities. After many summer schools and workshops, in particular over the last decade, the community of nuclear molecular physicists has decided to collaborate in producing a comprehensive collection of lectures and tutorial reviews covering the field. This third volume follows the successful Lect. Notes Phys. 818 (Vol. 1) and 848 (Vol. 2), and comprises six extensive lectures covering the following topics:  - Gamma Rays and Molecular Structure - Faddeev Equation Approach for Three Cluster Nuclear Reactions - Tomography of the Cluster Structure of Light Nuclei Via Relativistic Dissociation - Clustering Effects Within the Dinuclear Model : From Light to Hyper-heavy Molecules in Dynamical Mean-field Approach - Clusterization in Ternary Fission - Clusters in Light N...

  8. Spatial cluster modelling

    Lawson, Andrew B

    2002-01-01

    Research has generated a number of advances in methods for spatial cluster modelling in recent years, particularly in the area of Bayesian cluster modelling. Along with these advances has come an explosion of interest in the potential applications of this work, especially in epidemiology and genome research. In one integrated volume, this book reviews the state-of-the-art in spatial clustering and spatial cluster modelling, bringing together research and applications previously scattered throughout the literature. It begins with an overview of the field, then presents a series of chapters that illuminate the nature and purpose of cluster modelling within different application areas, including astrophysics, epidemiology, ecology, and imaging. The focus then shifts to methods, with discussions on point and object process modelling, perfect sampling of cluster processes, partitioning in space and space-time, spatial and spatio-temporal process modelling, nonparametric methods for clustering, and spatio-temporal ...

  9. Evolution of Galaxy and Quasar Clustering

    Bagla, J. S.

    1997-01-01

    We study the evolution of correlation function of dark matter halos in the CDM class of models. We show that the halo correlation function does not evolve in proportion with the correlation function of the underlying mass distribution. Earliest halos to collapse, which correspond to rare peaks in the density field, cluster very strongly. The amplitude of halo correlation function decreases from its initial, large, value. This decrease continues till the average peaks have collapsed, after whi...

  10. Agricultural Clusters in the Netherlands

    Schouten, M.A.; Heijman, W.J.M.

    2012-01-01

    Michael Porter was the first to use the term cluster in an economic context. He introduced the term in The Competitive Advantage of Nations (1990). The term cluster is also known as business cluster, industry cluster, competitive cluster or Porterian cluster. This article aims at determining and mea

  11. Endogenous Small RNA Clusters in Plants

    Yong-Xin Liu; Meng Wang; Xiu-Jie Wang

    2014-01-01

    In plants, small RNAs (sRNAs) usually refer to non-coding RNAs (ncRNAs) with lengths of 20-24 nucleotides. sRNAs are involved in the regulation of many essential processes related to plant development and environmental responses. sRNAs in plants are mainly grouped into microRNAs (miRNAs) and small interfering RNAs (siRNAs), and the latter can be further classified into trans-acting siRNAs (ta-siRNAs), repeat-associated siRNAs (ra-siRNAs), natural anti-sense siRNAs (nat-siRNAs), etc. Many sRNAs exhibit a clustered distribution pattern in the genome. Here, we summarize the features and functions of cluster-distributed sRNAs, aimed to not only provide a thorough picture of sRNA clusters (SRCs) in plants, but also shed light on the identification of new classes of functional sRNAs.

  12. Zipf's Law and the Universality Class of the Fragmentation Phase Transition

    Bauer, Wolfgang; Pratt, Scott; Alleman, Brandon

    2005-01-01

    We show that Zipf's Law for the largest clusters is not valid in an exact sense at the critical point of the fragmentation phase transition, contrary to previous claims. Instead, the extracted distributions of the largest clusters reflects the choice of universality class through the value of the critical exponent tau.

  13. theories of class

    Gaiotto, Davide; Razamat, Shlomo S.

    2015-07-01

    We construct classes of superconformal theories elements of which are labeled by punctured Riemann surfaces. Degenerations of the surfaces correspond, in some cases, to weak coupling limits. Different classes are labeled by two integers ( N, k). The k = 1 case coincides with A N - 1 theories of class and simple examples of theories with k > 1 are orbifolds of some of the A N - 1 class theories. For the space of theories to be complete in an appropriate sense we find it necessary to conjecture existence of new strongly coupled SCFTs. These SCFTs when coupled to additional matter can be related by dualities to gauge theories. We discuss in detail the A 1 case with k = 2 using the supersymmetric index as our analysis tool. The index of theories in classes with k > 1 can be constructed using eigenfunctions of elliptic quantum mechanical models generalizing the Ruijsenaars-Schneider integrable model. When the elliptic curve of the model degenerates these eigenfunctions become polynomials with coefficients being algebraic expressions in fugacities, generalizing the Macdonald polynomials with rational coefficients appearing when k = 1.

  14. Does class attendance still matter?

    Abel Nyamapfene

    2010-01-01

    This paper presents a study on the impact of class attendance on academic performance in a second year Electronics Engineering course module with online notes and no mandatory class attendance policy. The study shows that class attendance is highly correlated to academic performance, despite the availability of online class notes. In addition, there is significant correlation between class attendance and non-class contact with the lecturer and between student performance in the first year of ...

  15. Two steps in the evolution of Antennapedia-class vertebrate homeobox genes.

    Kappen, C. (Christian); Schughart, K; Ruddle, F H

    1989-01-01

    Antennapedia-class vertebrate homeobox genes have been classified with regard to their chromosomal locations and nucleotide sequence similarities within the 183-base-pair homeobox domain. The results of these comparisons support the view that in mammals and most likely the vertebrates, four clusters of homeobox genes exist that were created by duplications of an entire primordial gene cluster. We present evidence that this primordial cluster arose by local gene duplications of homeoboxes that...

  16. Translation in ESL Classes

    Nagy Imola Katalin

    2015-12-01

    Full Text Available The problem of translation in foreign language classes cannot be dealt with unless we attempt to make an overview of what translation meant for language teaching in different periods of language pedagogy. From the translation-oriented grammar-translation method through the complete ban on translation and mother tongue during the times of the audio-lingual approaches, we have come today to reconsider the role and status of translation in ESL classes. This article attempts to advocate for translation as a useful ESL class activity, which can completely fulfil the requirements of communicativeness. We also attempt to identify some activities and games, which rely on translation in some books published in the 1990s and the 2000s.

  17. Spatial Scan Statistic: Selecting clusters and generating elliptic clusters

    Christiansen, Lasse Engbo; Andersen, Jens Strodl

    2004-01-01

    The spatial scan statistic is widely used to search for clusters. This paper shows that the usually applied elimination of overlapping clusters to find secondary clusters is sensitive to smooth changes in the shape of the clusters. We present an algorithm for generation of set of confocal elliptic...... clusters. In addition, we propose a new way to present the information in a given set of clusters based on the significance of the clusters....

  18. Cluster brand as a competitive advantage. Case: Airport cluster Finland

    Väinölä, Lotta-Elviira

    2015-01-01

    Objective of the Study: The objective of this study is to explore the phenomenon of cluster branding. This study investigates cluster brand as a competitive advantage that impacts the success or decline of the cluster. The research questions examine three aspects: (1) cluster branding as a process, (2) the concrete tools that can be used in cluster branding and (3) the perceived benefits of cluster brand. The study aims to produce a generic model for cluster branding, which can be used as...

  19. Integrating cluster formation and cluster evaluation in interactive visual analysis

    Turkay, C.; Parulek, J.; Reuter, N.; Hauser, H.

    2011-01-01

    Cluster analysis is a popular method for data investigation where data items are structured into groups called clusters. This analysis involves two sequential steps, namely cluster formation and cluster evaluation. In this paper, we propose the tight integration of cluster formation and cluster evaluation in interactive visual analysis in order to overcome the challenges that relate to the black-box nature of clustering algorithms. We present our conceptual framework in the form of an interac...

  20. Clustering Categorical Data:A Cluster Ensemble Approach

    He Zengyou(何增友); Xu Xiaofei; Deng Shengchun

    2003-01-01

    Clustering categorical data, an integral part of data mining,has attracted much attention recently. In this paper, the authors formally define the categorical data clustering problem as an optimization problem from the viewpoint of cluster ensemble, and apply cluster ensemble approach for clustering categorical data. Experimental results on real datasets show that better clustering accuracy can be obtained by comparing with existing categorical data clustering algorithms.

  1. Classıfıcatıon of Dıstrıct TR72 Towns wıth Fuzzy Clusterıng Analysıs Usıng Socıo-Economıc Data

    Erilli, N. Alp

    2014-01-01

    In economy policies, socioeconomic indicators have an important place in determining the development levels. The determination and classification of the current social and economic structure of cities and districts are considerably important in analyzing the development of cities and districts in their probable development tendencies and in developing the regional development policies in parallel to this. It is essential to use fuzzy clustering analysis, whether clusters separated well or the...

  2. Coping With New Challengens for Density-Based Clustering

    Kröger, Peer

    2004-01-01

    Knowledge Discovery in Databases (KDD) is the non-trivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data. The core step of the KDD process is the application of a Data Mining algorithm in order to produce a particular enumeration of patterns and relationships in large databases. Clustering is one of the major data mining tasks and aims at grouping the data objects into meaningful classes (clusters) such that the similarity of objects wi...

  3. Bridging the gap between cluster and grid computing

    Alves, Albano; Pina, António

    2006-01-01

    The Internet computing model with its ubiquitous networking and computing infrastructure is driving a new class of interoperable applications that benefit both from high computing power and multiple Internet connections. In this context, grids are promising computing platforms that allow to aggregate distributed resources such as workstations and clusters to solve large-scale problems. However, because most parallel programming tools were primarily developed for MPP and cluster computing, to ...

  4. Hydrometeor classification from polarimetric radar measurements: a clustering approach

    Grazioli, Jacopo; Tuia, Devis; Berne, Alexis

    2015-01-01

    A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number of hydrometeor classes (nopt) that can be reliably identified from a large set of polarimetric data is determined. This is done by means of an unsupervised clustering technique guided by criteria related both to data similarity and to spatial smoothness of the classified images. In a second step, the nopt clusters are assign...

  5. Class hierarchy method to find Change-Proneness

    Malan V.Gaikwad

    2011-01-01

    Full Text Available Finding Proneness of software is necessary to identify fault prone and change prone classes at earlier stages of development, so that those classes can be given special attention. Also to improves the quality and reliability of the software. For corrective and adaptive maintenance we require to make changes during the software evolution.As such changes cluster around number of key components in software, it is important to analyze the frequency of changes in individual classes and also to identify and show related changes in multiple classes. Early detection of fault prone and change prone classes can enables the developers and experts to spend their valuable time and resources on these areas of software. Prediction of change-prone and fault prone classes of a software is an active topic in the area of software engineering. Such prediction can be used to predict changes to different classes of a system from one release of software to the next release. Identifying the change-prone and fault prone classes in advance can helps to focus attention on these classes.In this paper we are focusing on finding dependency of software that can be chieved by estimating the proneness of Object Oriented Software. Two main types of proneness are associated with OO software. Fault Proneness and Change Proneness.

  6. BIO 315 Complete Class

    admn

    2015-01-01

    BIO 315 Complete Class Check this A+ tutorial guideline at   http://www.assignmentcloud.com/BIO-315/BIO-315-Complete-Class BIO 315 Week 1 DQ 1 BIO 315 Week 1 DQ 2 BIO 315 Week 1 Individual Assignment Beren Robinson Field Study Paper BIO 315 Week 2 DQ 1 BIO 315 Week 2 DQ 2 BIO 315 Week 2 DQ 3 BIO 315 Week 2 Individual Assignment Environment Resources and Competition BIO 315 Week 2 Week Two Learning Team Exercises BIO 315 Week 3 DQ 1 BIO ...

  7. Class actions in Portugal

    Raimundo, Maria Carlos Miranda

    2013-01-01

    Even eighteen years after the implementation of Law 83/95, of August 31, about the rights of participation in class action litigation in Portugal, there is no sufficient evidence of its applicability. Contrarily to what is observed in other countries as the United States and Brazil it seems that in Portugal, there is no interest of the several parties that would be involved in a class action litigation to obtain information or inform other parties relatively to the main procedures on it and t...

  8. An "expanded" class perspective

    Steur, Luisa Johanna

    2014-01-01

    Adivasis against their age-old colonization or the work of ‘external’ agitators. Capitalist restructuring and ‘globalization’ was generally seen as simply the latest chapter in the suffering of these Adivasis. Little focused attention was paid to the recent class trajectory of their lives under changing...... analysis, as elaborated in Marxian anthropology, this article provides an alternative to the liberal-culturalist explanation of indigenism in Kerala, arguing instead that contemporary class processes—as experienced close to the skin by the people who decided to participate in the Muthanga struggle...

  9. Talking Class in Tehroon

    Elling, Rasmus Christian; Rezakhani, Khodadad

    2016-01-01

    Persian, like any other language, is laced with references to class, both blatant and subtle. With idioms and metaphors, Iranians can identify and situate others, and thus themselves, within hierarchies of social status and privilege, both real and imagined. Some class-related terms can be traced...... back to medieval times, whereas others are of modern vintage, the linguistic legacy of television shows, pop songs, social media memes or street vernacular. Every day, it seems, an infectious set of phrases appears that make yesterday’s seem embarrassingly antiquated....

  10. Residues of Chern classes

    Suwa, Tatsuo; 諏訪, 立雄

    2003-01-01

    If we have a finite number of sections of a complex vector bundle E over a manifold M, certain Chern classes of E are localized at the singular set S, i.e., the set of points where the sections fail to be linearly independent. When S is compact, the localizations define the residues at each connected component of S by the Alexander duality. If M itself is compact, the sum of the residues is equal to the Poincaré dual of the corresponding Chern class. This type of theory is also developed for ...

  11. Residues of Chern classes

    Suwa, Tatsuo

    2003-01-01

    If we have a finite number of sections of a complex vector bundle $E$ over a manifold $M$ , certain Chern classes of $E$ are localized at the singular set $S$ , i.e., the set of points where the sections fail to be linearly independent. When $S$ is compact, the localizations define the residues at each connected component of $S$ by the Alexander duality. If $M$ itself is compact, the sum of the residues is equal to the Poincaré dual of the corresponding Chern class. This type of theory is als...

  12. Disentangling Porterian Clusters

    Jagtfelt, Tue

    This dissertation investigates the contemporary phenomenon of industrial clusters based on the work of Michael E. Porter, the central progenitor and promoter of the cluster notion. The dissertation pursues two central questions: 1) What is a cluster? and 2) How could Porter’s seemingly fuzzy......, contested theory become so widely disseminated and applied as a normative and prescriptive strategy for economic development? The dissertation traces the introduction of the cluster notion into the EU’s Lisbon Strategy and demonstrates how its inclusion originates from Porter’s colleagues: Professor Örjan...... Sölvell, Dr. Christian Ketels and Dr. Göran Lindqvist. Taking departure in Porter’s works and the cluster literature, the dissertations shows a considerable paradigmatic shift has occurred from the first edition of Nations to the present state of cluster cooperation. To elaborate on this change and the...

  13. From collisions to clusters

    Loukonen, Ville; Bork, Nicolai; Vehkamaki, Hanna

    2014-01-01

    overcome the possible initial non-optimal collision orientations. No post-collisional cluster break up is observed. The reasons for the efficient clustering are (i) the proton transfer reaction which takes place in each of the collision simulations and (ii) the subsequent competition over the proton......The clustering of sulphuric acid with base molecules is one of the main pathways of new-particle formation in the Earth's atmosphere. First step in the clustering process is likely the formation of a (sulphuric acid)1(base)1(water)n cluster. Here, we present results from direct first......-principles molecular dynamics collision simulations of (sulphuric acid)1(water)0, 1 + (dimethylamine) → (sulphuric acid)1(dimethylamine)1(water)0, 1 cluster formation processes. The simulations indicate that the sticking factor in the collisions is unity: the interaction between the molecules is strong enough to...

  14. Cosmology with cluster surveys

    Subhabrata Majumdar

    2004-10-01

    Surveys of clusters of galaxies provide us with a powerful probe of the density and nature of the dark energy. The red-shift distribution of detected clusters is highly sensitive to the dark energy equation of state parameter . Upcoming Sunyaev–Zel'dovich (SZ) surveys would provide us large yields of clusters to very high red-shifts. Self-calibration of cluster scaling relations, possible for such a huge sample, would be able to constrain systematic biases on mass estimators. Combining cluster red-shift abundance with limited mass follow-up and cluster mass power spectrum can then give constraints on , as well as on 8 and to a few per cents.

  15. Cluster Management Institutionalization

    Normann, Leo; Agger Nielsen, Jeppe

    2015-01-01

    This article explores a new management form – cluster management – in Danish public sector day care. Although cluster management has been widely adopted in Danish day care at the municipality level, it has attracted only sparse research attention. We use theoretical insights from Scandinavian...... institutionalism together with a longitudinal case-based inquiry into how cluster management has entered and penetrated the management practices of day care in Denmark. We demonstrate how cluster management became widely adopted in the day care field not only because of its intrinsic properties but also because...... of how it was legitimized as a “ready-to-use” management model. Further, our account reveals how cluster management translated into considerably different local variants as it travelled into specific organizations. However, these processes have not occurred sequentially with cluster management first...

  16. Nanophase materials assembled from clusters

    Siegel, R.W.

    1992-02-01

    The preparation of metal and ceramic atom clusters by means of the gas-condensation method, followed by their in situ collection and consolidation under high-vacuum conditions, has recently led to the synthesis of a new class of ultrafine-grained materials. These nanophase materials, with typical average grain sizes of 5 to 50 nm and, hence, a large fraction of their atoms in interfaces, exhibit properties that are often considerably improved relative to those of conventional materials. Furthermore, their synthesis and processing characteristics should enable the design of new materials with unique properties. Some examples are ductile ceramics that can be formed and sintered to full density at low temperatures without the need for binding or sintering aids, and metals with dramatically increased strength. The synthesis of these materials is briefly described along with what is presently known of their structure and properties. Their future impact on materials science and technology is also considered.

  17. Clustering Techniques in Bioinformatics

    Muhammad Ali Masood

    2015-01-01

    Full Text Available Dealing with data means to group information into a set of categories either in order to learn new artifacts or understand new domains. For this purpose researchers have always looked for the hidden patterns in data that can be defined and compared with other known notions based on the similarity or dissimilarity of their attributes according to well-defined rules. Data mining, having the tools of data classification and data clustering, is one of the most powerful techniques to deal with data in such a manner that it can help researchers identify the required information. As a step forward to address this challenge, experts have utilized clustering techniques as a mean of exploring hidden structure and patterns in underlying data. Improved stability, robustness and accuracy of unsupervised data classification in many fields including pattern recognition, machine learning, information retrieval, image analysis and bioinformatics, clustering has proven itself as a reliable tool. To identify the clusters in datasets algorithm are utilized to partition data set into several groups based on the similarity within a group. There is no specific clustering algorithm, but various algorithms are utilized based on domain of data that constitutes a cluster and the level of efficiency required. Clustering techniques are categorized based upon different approaches. This paper is a survey of few clustering techniques out of many in data mining. For the purpose five of the most common clustering techniques out of many have been discussed. The clustering techniques which have been surveyed are: K-medoids, K-means, Fuzzy C-means, Density-Based Spatial Clustering of Applications with Noise (DBSCAN and Self-Organizing Map (SOM clustering.

  18. Cluster Symmetries and Dynamics

    Freer Martin

    2016-01-01

    Full Text Available Many light nuclei display behaviour that indicates that rather than behaving as an A-body systems, the protons and neutrons condense into clusters. The α-particle is the most obvious example of such clustering. This contribution examines the role of such α-clustering on the structure, symmetries and dynamics of the nuclei 8Be, 12C and 16O, recent experimental measurements and future perspectives.

  19. Agricultural Clusters in China

    Kiminami, Lily; Kiminami, Akira

    2009-01-01

    The purpose of this study is to assess the potential of clustering in the development of agriculture and rural communities in China. We shall examine in detail the food industry, which is the link in the food chain that propels the industrialization of agriculture, and identify instances of industrial agglomeration and business collaboration. Next, we shall analyze the externalities (i.e. spillovers) of clusters, demand conditions in cluster formation, and the effectiveness of business collab...

  20. The Durban Auto Cluster

    Lorentzen, Jochen; Robbins, Glen; Barnes, Justin

    2004-01-01

    The paper describes the formation of the Durban Auto Cluster in the context of trade liberalization. It argues that the improvement of operational competitiveness of firms in the cluster is prominently due to joint action. It tests this proposition by comparing the gains from cluster activities in the areas of supplier development, human resource development, logistics, and benchmarking, and by contrasting the impact of joint action against a host of other variables, notably international com...

  1. Securing personal network clusters

    Jehangir, Assed; Heemstra de Groot, Sonia M.

    2007-01-01

    A Personal Network is a self-organizing, secure and private network of a user’s devices notwithstanding their geographic location. It aims to utilize pervasive computing to provide users with new and improved services. In this paper we propose a model for securing Personal Network clusters. Clusters are ad-hoc networks of co-located personal devices. The ad-hoc makeup of clusters, coupled with the resource constrained nature of many constituent devices, makes enforcing security a challenging ...

  2. Cluster headache with aura

    Martínez-Fernández, Eva; Alberca, Roman; Mir, Pablo; Franco, Emilio; Montes, Enrique; Lozano, Pilar

    2002-01-01

    The objective of our study is to report the frequency and characteristics of cluster headache with aura among the population of patients with cluster headache treated in our outpatient neurology clinic. 254 patients were submitted to semi-structured interviews to identify the presence of symptoms similar to the migraine aura. 5 patients who suffered from a cluster headache with aura filled a diary with the characteristics of the pain attacks and the aura. All the patients with either episodic...

  3. A Study of the Classification Capabilities of Neural Networks Using Unsupervised Learning: A Comparison with K-Means Clustering.

    Balakrishnan, P. V. (Sunder); And Others

    1994-01-01

    A simulation study compares nonhierarchical clustering capabilities of a class of neural networks using Kohonen learning with a K-means clustering procedure. The focus is on the ability of the procedures to recover correctly the known cluster structure in the data. Advantages and disadvantages of the procedures are reviewed. (SLD)

  4. 15th Cluster workshop

    Laakso, Harri; Escoubet, C. Philippe; The Cluster Active Archive : Studying the Earth’s Space Plasma Environment

    2010-01-01

    Since the year 2000 the ESA Cluster mission has been investigating the small-scale structures and processes of the Earth's plasma environment, such as those involved in the interaction between the solar wind and the magnetospheric plasma, in global magnetotail dynamics, in cross-tail currents, and in the formation and dynamics of the neutral line and of plasmoids. This book contains presentations made at the 15th Cluster workshop held in March 2008. It also presents several articles about the Cluster Active Archive and its datasets, a few overview papers on the Cluster mission, and articles reporting on scientific findings on the solar wind, the magnetosheath, the magnetopause and the magnetotail.

  5. Management of cluster headache.

    Tfelt-Hansen, Peer C; Jensen, Rigmor H

    2012-07-01

    The prevalence of cluster headache is 0.1% and cluster headache is often not diagnosed or misdiagnosed as migraine or sinusitis. In cluster headache there is often a considerable diagnostic delay - an average of 7 years in a population-based survey. Cluster headache is characterized by very severe or severe orbital or periorbital pain with a duration of 15-180 minutes. The cluster headache attacks are accompanied by characteristic associated unilateral symptoms such as tearing, nasal congestion and/or rhinorrhoea, eyelid oedema, miosis and/or ptosis. In addition, there is a sense of restlessness and agitation. Patients may have up to eight attacks per day. Episodic cluster headache (ECH) occurs in clusters of weeks to months duration, whereas chronic cluster headache (CCH) attacks occur for more than 1 year without remissions. Management of cluster headache is divided into acute attack treatment and prophylactic treatment. In ECH and CCH the attacks can be treated with oxygen (12 L/min) or subcutaneous sumatriptan 6 mg. For both oxygen and sumatriptan there are two randomized, placebo-controlled trials demonstrating efficacy. In both ECH and CCH, verapamil is the prophylactic drug of choice. Verapamil 360 mg/day was found to be superior to placebo in one clinical trial. In clinical practice, daily doses of 480-720 mg are mostly used. Thus, the dose of verapamil used in cluster headache treatment may be double the dose used in cardiology, and with the higher doses the PR interval should be checked with an ECG. At the start of a cluster, transitional preventive treatment such as corticosteroids or greater occipital nerve blockade can be given. In CCH and in long-standing clusters of ECH, lithium, methysergide, topiramate, valproic acid and ergotamine tartrate can be used as add-on prophylactic treatment. In drug-resistant CCH, neuromodulation with either occipital nerve stimulation or deep brain stimulation of the hypothalamus is an alternative treatment strategy

  6. Fast Density Based Clustering Algorithm

    Priyanka Trikha; Singh Vijendra

    2013-01-01

    Clustering problem is an unsupervised learning problem. It is a procedure that partition data objects into matching clusters. The data objects in the same cluster are quite similar to each other and dissimilar in the other clusters. The traditional algorithms do not meet the latest multiple requirements simultaneously for objects. Density-based clustering algorithms find clusters based on density of data points in a region. DBSCAN algorithm is one of the density-based clustering algorithms. I...

  7. Statistical Properties of Convex Clustering

    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 tuning parameter for convex clustering that yields a non-trivial solution. We also provide an unbiased estimate of the degrees of freedom, and provide a finite sample bound for the prediction error for convex clustering. We compare convex clustering to some tr...

  8. A Uniqueness Theorem for Clustering

    Zadeh, Reza Bosagh; Ben-David, Shai

    2012-01-01

    Despite the widespread use of Clustering, there is distressingly little general theory of clustering available. Questions like "What distinguishes a clustering of data from other data partitioning?", "Are there any principles governing all clustering paradigms?", "How should a user choose an appropriate clustering algorithm for a particular task?", etc. are almost completely unanswered by the existing body of clustering literature. We consider an axiomatic approach to the theory of Clustering...

  9. Statistical properties of convex clustering

    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...

  10. Optimization-Based Model Fitting for Latent Class and Latent Profile Analyses

    Huang, Guan-Hua; Wang, Su-Mei; Hsu, Chung-Chu

    2011-01-01

    Statisticians typically estimate the parameters of latent class and latent profile models using the Expectation-Maximization algorithm. This paper proposes an alternative two-stage approach to model fitting. The first stage uses the modified k-means and hierarchical clustering algorithms to identify the latent classes that best satisfy the…

  11. Document Clustering Based on Semi-Supervised Term Clustering

    Hamid Mahmoodi

    2012-05-01

    Full Text Available The study is conducted to propose a multi-step feature (term selection process and in semi-supervised fashion, provide initial centers for term clusters. Then utilize the fuzzy c-means (FCM clustering algorithm for clustering terms. Finally assign each of documents to closest associated term clusters. While most text clustering algorithms directly use documents for clustering, we propose to first group the terms using FCM algorithm and then cluster documents based on terms clusters. We evaluate effectiveness of our technique on several standard text collections and compare our results with the some classical text clustering algorithms.

  12. Teaching Very Large Classes

    DeRogatis, Amy; Honerkamp, Kenneth; McDaniel, Justin; Medine, Carolyn; Nyitray, Vivian-Lee; Pearson, Thomas

    2014-01-01

    The editor of "Teaching Theology and Religion" facilitated this reflective conversation with five teachers who have extensive experience and success teaching extremely large classes (150 students or more). In the course of the conversation these professors exchange and analyze the effectiveness of several active learning strategies they…

  13. Reference class forecasting

    Flyvbjerg, Bent

    Underbudgettering og budgetoverskridelser forekommer i et flertal af større bygge- og anlægsprojekter. Problemet skyldes optimisme og/eller strategisk misinformation i budgetteringsprocessen. Reference class forecasting (RCF) er en prognosemetode, som er udviklet for at reducere eller eliminere...

  14. Fostering a Middle Class

    2011-01-01

    Though there is no official definition of "middle class" in China, the tag has become one few Chinese people believe they deserve anyway.In early August, the Chinese Academy of Social Sciences released a report on China’s urban development,

  15. Second class weak currents

    The definition and general properties of weak second class currents are recalled and various detection possibilities briefly reviewed. It is shown that the existing data on nuclear beta decay can be consistently analysed in terms of a phenomenological model. Their implication on the fundamental structure of weak interactions is discussed

  16. Class Actions in Denmark

    Werlauff, Erik

    2009-01-01

    The article deals with the relatively new Danish Act on Class Action (Danish: gruppesøgsmål) which was suggested by The Permanent Council on Civil procedure (Retsplejerådet) of which the article's author is a member. The operability of the new provisions is illustrated through some wellknown Danish...

  17. Adeus à classe trabalhadora?

    Geoff Eley

    2013-12-01

    Full Text Available No início da década de 1980, a política centrada em classes da tradição socialista estava em crise, e comentadores importantes adotaram tons apocalípticos. No final da década, a esquerda permanecia profundamente dividida entre os advogados da mudança e os defensores da fé. Em meados dos anos 1990, os primeiros tinham, de modo geral, ganhado a batalha. O artigo busca apresentar essa mudança contemporânea não como a 'morte da classe', mas como o desa­parecimento de um tipo particular de ­sociedade de classes, marcado pelo ­processo de formação da classe trabalhadora entre os anos 1880 e 1940 e pelo alinhamento político daí resultante, atingindo seu apogeu na construção social-democrata do acordo do pós-guerra. Quando mudanças de longo prazo na economia se combinaram com o ataque ao keynesianismo na política de recessão a partir de meados da década de 1970, a unidade da classe trabalhadora deixou de estar disponível da forma antiga e bastante utilizada, como o terreno natural da política de esquerda. Enquanto uma coletividade dominante da classe trabalhadora entrou em declínio, outra se corporificou de modo lento e desigual para tomar o lugar daquela. Mas a unidade operacional dessa nova agregação da classe trabalhadora ainda está, em grande parte, em formação. Para recuperar a eficácia política da tradição socialista, alguma nova visão de agência política coletiva será necessária, uma visão imaginativamente ajustada às condições emergentes da produção e acumulação capitalista no início do século XXI.

  18. Lifting to cluster-tilting objects in higher cluster categories

    Liu, Pin

    2008-01-01

    In this note, we consider the $d$-cluster-tilted algebras, the endomorphism algebras of $d$-cluster-tilting objects in $d$-cluster categories. We show that a tilting module over such an algebra lifts to a $d$-cluster-tilting object in this $d$-cluster category.

  19. Critical exponents from cluster coefficients.

    Rotman, Z; Eisenberg, E

    2009-09-01

    For a large class of repulsive interaction models, the Mayer cluster integrals can be transformed into a tridiagonal real symmetric matrix R_{mn} , whose elements converge to two constants. This allows for an effective extrapolation of the equation of state for these models. Due to a nearby (nonphysical) singularity on the negative real z axis, standard methods (e.g., Padé approximants based on the cluster integrals expansion) fail to capture the behavior of these models near the ordering transition, and, in particular, do not detect the critical point. A recent work [E. Eisenberg and A. Baram, Proc. Natl. Acad. Sci. U.S.A. 104, 5755 (2007)] has shown that the critical exponents sigma and sigma;{'} , characterizing the singularity of the density as a function of the activity, can be exactly calculated if the decay of the R matrix elements to their asymptotic constant follows a 1/n;{2} law. Here we employ renormalization group (RG) arguments to extend this result and analyze cases for which the asymptotic approach of the R matrix elements toward their limiting value is of a more general form. The relevant asymptotic correction terms (in RG sense) are identified, and we then present a corrected exact formula for the critical exponents. We identify the limits of usage of the formula and demonstrate one physical model, which is beyond its range of validity. The formula is validated numerically and then applied to analyze a number of concrete physical models. PMID:19905081

  20. Fuzzy Clustering: Determining the Number of Clusters

    Řezanková, H.; Húsek, Dušan

    Piscataway : IEEE, 2012, s. 277-282. ISBN 978-1-4673-4793-8. [CASoN 2012. International Conference on Computational Aspects of Social Networks /4./. Sao Carlos (BR), 21.11.2012-23.11.2012] R&D Projects: GA ČR GAP202/10/0262 Grant ostatní: GA MŠk(CZ) ED1.1.00/02.0070 Institutional support: RVO:67985807 Keywords : fuzzy cluster analysis * determining number of clusters * Dunn’s coefficient * average silhouette width Subject RIV: BB - Applied Statistics, Operational Research

  1. Multipartite cluster entangled states for continuous variables via quantum interference

    Continuous variable cluster entangled states are a potential resource for universal quantum computation. Here we propose a scalable scheme to prepare a class of multimode cluster entangled states. In terms of graph states, all modes are denoted by the nodes, and the lines connecting the nodes represent the interactions between the connected nodes. Our cluster entangled states correspond to two-colourable graphs, in which the nodes belong to two different families, and the lines connect only the nodes of different families. The physical mechanism is attributed to quantum interference between multiple pathways for wave-mixing parametric interactions in near-resonant systems.

  2. Issues,Challenges and Tools of Clustering Algorithms

    Agarwal, Parul; Biswas, Ranjit

    2011-01-01

    Clustering is an unsupervised technique of Data Mining. It means grouping similar objects together and separating the dissimilar ones. Each object in the data set is assigned a class label in the clustering process using a distance measure. This paper has captured the problems that are faced in real when clustering algorithms are implemented .It also considers the most extensively used tools which are readily available and support functions which ease the programming. Once algorithms have been implemented, they also need to be tested for its validity. There exist several validation indexes for testing the performance and accuracy which have also been discussed here.

  3. Concurrent conditional clustering of multiple networks: COCONETS.

    Sabrina Kleessen

    Full Text Available The accumulation of high-throughput data from different experiments has facilitated the extraction of condition-specific networks over the same set of biological entities. Comparing and contrasting of such multiple biological networks is in the center of differential network biology, aiming at determining general and condition-specific responses captured in the network structure (i.e., included associations between the network components. We provide a novel way for comparison of multiple networks based on determining network clustering (i.e., partition into communities which is optimal across the set of networks with respect to a given cluster quality measure. To this end, we formulate the optimization-based problem of concurrent conditional clustering of multiple networks, termed COCONETS, based on the modularity. The solution to this problem is a clustering which depends on all considered networks and pinpoints their preserved substructures. We present theoretical results for special classes of networks to demonstrate the implications of conditionality captured by the COCONETS formulation. As the problem can be shown to be intractable, we extend an existing efficient greedy heuristic and applied it to determine concurrent conditional clusters on coexpression networks extracted from publically available time-resolved transcriptomics data of Escherichia coli under five stresses as well as on metabolite correlation networks from metabolomics data set from Arabidopsis thaliana exposed to eight environmental conditions. We demonstrate that the investigation of the differences between the clustering based on all networks with that obtained from a subset of networks can be used to quantify the specificity of biological responses. While a comparison of the Escherichia coli coexpression networks based on seminal properties does not pinpoint biologically relevant differences, the common network substructures extracted by COCONETS are supported by

  4. The Cluster Substructure - Alignment Connection

    Plionis, M

    2002-01-01

    Using the APM cluster data 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 cluster develop in a hierarchical fashion by anisotropy merging along the large-scale filamentary superclusters within which they are embedded.

  5. Generalised Brown Clustering and Roll-up Feature Generation

    Derczynski, Leon; Chester, Sean

    2016-01-01

    Brown clustering is an established technique, used in hundreds of computational linguistics papers each year, to group word types that have similar distributional information. It is unsupervised and can be used to create powerful word representations for machine learning. Despite its improbable...... success relative to more complex methods, few have investigated whether Brown clustering has really been applied optimally. In this paper, we present a subtle but profound generalisation of Brown clustering to improve the overall quality by decoupling the number of output classes from the computational...... active set size. Moreover, the generalisation permits a novel approach to feature selection from Brown clusters: We show that the standard approach of shearing the Brown clustering output tree at arbitrary bitlengths is lossy and that features should be chosen instead by rolling up Generalised Brown...

  6. Electron Detachment and Subsequent Structural Changes of Water Clusters.

    Das, Susanta; Sengupta, Turbasu; Dutta, Achintya Kumar; Pal, Sourav

    2016-02-25

    A cost-effective equation of motion coupled cluster method, EOMIP-CCSD(2), is used to investigate vertical and adiabatic ionization potential as well as ionization-induced structural changes of water clusters and compared with CCSD(T), CASPT2, and MP2 methods. The moderate N(5) scaling and low storage requirement yields EOMIP-CCSD(2) calculation feasible even for reasonably large molecules and clusters with accuracy comparable to CCSD(T) method at much cheaper computational cost. Our calculations shed light on the authenticity of EOMIP-CCSD(2) results and establish a reliable method to study of ionization energy of molecular clusters. We have further investigated the performance of several classes of DFT functionals for ionization energies of water clusters to benchmark the results and to get a reliable functionals for the same. PMID:26835702

  7. Comparison between optical and X-ray cluster detection methods

    Basilakos, S; Georgakakis, A; Georgantopoulos, I; Gaga, T; Kolokotronis, V G; Stewart, G C

    2003-01-01

    In this work we present combined optical and X-ray cluster detection methods in an area near the North Galactic Pole area, previously covered by the SDSS and 2dF optical surveys. The same area has been covered by shallow ($\\sim 1.8$ deg$^{2}$) XMM-{\\em Newton} observations. The optical cluster detection procedure is based on merging two independent selection methods - a smoothing+percolation technique, and a Matched Filter Algorithm. The X-ray cluster detection is based on a wavelet-based algorithm, incorporated in the SAS v.5.2 package. The final optical sample counts 9 candidate clusters with richness of more than 20 galaxies, corresponding roughly to APM richness class. Three, of our optically detected clusters are also detected in our X-ray survey.

  8. Fuzzy Document Clustering Approach using WordNet Lexical Categories

    Gharib, Tarek F.; Fouad, Mohammed M.; Aref, Mostafa M.

    Text mining refers generally to the process of extracting interesting information and knowledge from unstructured text. This area is growing rapidly mainly because of the strong need for analysing the huge and large amount of textual data that reside on internal file systems and the Web. Text document clustering provides an effective navigation mechanism to organize this large amount of data by grouping their documents into a small number of meaningful classes. In this paper we proposed a fuzzy text document clustering approach using WordNet lexical categories and Fuzzy c-Means algorithm. Some experiments are performed to compare efficiency of the proposed approach with the recently reported approaches. Experimental results show that Fuzzy clustering leads to great performance results. Fuzzy c-means algorithm overcomes other classical clustering algorithms like k-means and bisecting k-means in both clustering quality and running time efficiency.

  9. Dynamical Mass Measurements of Contaminated Galaxy Clusters Using Machine Learning

    Ntampaka, M; Sutherland, D J; Fromenteau, S; Poczos, B; Schneider, J

    2015-01-01

    We study dynamical mass measurements of galaxy clusters contaminated by interlopers and show that a modern machine learning (ML) algorithm can predict masses by better than a factor of two compared to a standard scaling relation approach. We create two mock catalogs from Multidark's publicly-available N-body MDPL1 simulation, one with perfect galaxy cluster membership information and the other where a simple cylindrical cut around the cluster center allows interlopers to contaminate the clusters. In the standard approach, we use a power law scaling relation to infer cluster mass from galaxy line of sight (LOS) velocity dispersion. Assuming perfect membership knowledge, this unrealistic case produces a wide fractional mass error distribution, with width = 0.87. Interlopers introduce additional scatter, significantly widening the error distribution further (width = 2.13). We employ the Support Distribution Machine (SDM) class of algorithms to learn from distributions of data to predict single values. Applied to...

  10. Initialization independent clustering with actively self-training method.

    Nie, Feiping; Xu, Dong; Li, Xuelong

    2012-02-01

    The results of traditional clustering methods are usually unreliable as there is not any guidance from the data labels, while the class labels can be predicted more reliable by the semisupervised learning if the labels of partial data are given. In this paper, we propose an actively self-training clustering method, in which the samples are actively selected as training set to minimize an estimated Bayes error, and then explore semisupervised learning to perform clustering. Traditional graph-based semisupervised learning methods are not convenient to estimate the Bayes error; we develop a specific regularization framework on graph to perform semisupervised learning, in which the Bayes error can be effectively estimated. In addition, the proposed clustering algorithm can be readily applied in a semisupervised setting with partial class labels. Experimental results on toy data and real-world data sets demonstrate the effectiveness of the proposed clustering method on the unsupervised and the semisupervised setting. It is worthy noting that the proposed clustering method is free of initialization, while traditional clustering methods are usually dependent on initialization. PMID:22086542

  11. Using a Probabilistic Class-Based Lexicon for Lexical Ambiguity Resolution

    Prescher, Detlef; Riezler, Stefan; Rooth, Mats

    2000-01-01

    This paper presents the use of probabilistic class-based lexica for disambiguation in target-word selection. Our method employs minimal but precise contextual information for disambiguation. That is, only information provided by the target-verb, enriched by the condensed information of a probabilistic class-based lexicon, is used. Induction of classes and fine-tuning to verbal arguments is done in an unsupervised manner by EM-based clustering techniques. The method shows promising results in ...

  12. Mixed-Initiative Clustering

    Huang, Yifen

    2010-01-01

    Mixed-initiative clustering is a task where a user and a machine work collaboratively to analyze a large set of documents. We hypothesize that a user and a machine can both learn better clustering models through enriched communication and interactive learning from each other. The first contribution or this thesis is providing a framework of…

  13. Density Adaptive Parallel Clustering

    La Rocca, Marcello

    2014-01-01

    In this paper we are going to introduce a new nearest neighbours based approach to clustering, and compare it with previous solutions; the resulting algorithm, which takes inspiration from both DBscan and minimum spanning tree approaches, is deterministic but proves simpler, faster and doesnt require to set in advance a value for k, the number of clusters.

  14. Star Formation in Clusters

    Larsen, S S

    2004-01-01

    HST is very well tailored for observations of extragalactic star clusters. One obvious reason is HST's high spatial resolution, but equally important is the wavelength range offered by the instruments on board HST, in particular the blue and near-UV coverage which is essential for age-dating young clusters. HST observations have helped establish the ubiquity of young massive clusters (YMCs) in a wide variety of star-forming environments, from dwarf galaxies and spiral disks to nuclear starbursts and mergers. These YMCs have masses and sizes similar to those of old globular clusters (GCs), and the two may be closely related. A large fraction of all stars seem to be born in clusters, but most clusters disrupt rapidly and the stars disperse to become part of the field population. In most cases studied to date the luminosity functions of young cluster systems are well fit by power-laws dN(L)/dL ~ L^-2, and the luminosity of the brightest cluster can (with few exceptions) be predicted from simple sampling statisti...

  15. Coma cluster of galaxies

    1999-01-01

    Atlas Image mosaic, covering 34' x 34' on the sky, of the Coma cluster, aka Abell 1656. This is a particularly rich cluster of individual galaxies (over 1000 members), most prominently the two giant ellipticals, NGC 4874 (right) and NGC 4889 (left). The remaining members are mostly smaller ellipticals, but spiral galaxies are also evident in the 2MASS image. The cluster is seen toward the constellation Coma Berenices, but is actually at a distance of about 100 Mpc (330 million light years, or a redshift of 0.023) from us. At this distance, the cluster is in what is known as the 'Hubble flow,' or the overall expansion of the Universe. As such, astronomers can measure the Hubble Constant, or the universal expansion rate, based on the distance to this cluster. Large, rich clusters, such as Coma, allow astronomers to measure the 'missing mass,' i.e., the matter in the cluster that we cannot see, since it gravitationally influences the motions of the member galaxies within the cluster. The near-infrared maps the overall luminous mass content of the member galaxies, since the light at these wavelengths is dominated by the more numerous older stellar populations. Galaxies, as seen by 2MASS, look fairly smooth and homogeneous, as can be seen from the Hubble 'tuning fork' diagram of near-infrared galaxy morphology. Image mosaic by S. Van Dyk (IPAC).

  16. Computing upper cluster algebras

    Matherne, Jacob; Muller, Greg

    2013-01-01

    This paper develops techniques for producing presentations of upper cluster algebras. These techniques are suited to computer implementation, and will always succeed when the upper cluster algebra is totally coprime and finitely generated. We include several examples of presentations produced by these methods.

  17. Cluster growth kinetics

    Processes of some traffic blocking coming into existence are considered as probabilistic ones. We study analytic solutions for models for the dynamics of both cluster growth and cluster growth with fragmentation in the systems of finite number of objects. Assuming rates constancy of both coalescence and fragmentation, the models under consideration are linear on the probability functions

  18. Clustering Text Data Streams

    Yu-Bao Liu; Jia-Rong Cai; Jian Yin; Ada Wai-Chee Fu

    2008-01-01

    Clustering text data streams is an important issue in data mining community and has a number of applications such as news group filtering, text crawling, document organization and topic detection and tracing etc. However, most methods are similarity-based approaches and only use the TF*IDF scheme to represent the semantics of text data and often lead to poor clustering quality. Recently, researchers argue that semantic smoothing model is more efficient than the existing TF.IDF scheme for improving text clustering quality. However, the existing semantic smoothing model is not suitable for dynamic text data context. In this paper, we extend the semantic smoothing model into text data streams context firstly. Based on the extended model, we then present two online clustering algorithms OCTS and OCTSM for the clustering of massive text data streams. In both algorithms, we also present a new cluster statistics structure named cluster profile which can capture the semantics of text data streams dynamically and at the same time speed up the clustering process. Some efficient implementations for our algorithms are also given. Finally, we present a series of experimental results illustrating the effectiveness of our technique.

  19. Phases of cluster states

    The question of phases and phase transitions of cluster states is reviewed. First some features of the vibron model are recalled, then its extensions are investigated. Preliminary results are also presented from a study on the cluster-shell competition. (authors)

  20. Illinois' Career Cluster Model

    Jankowski, Natasha A.; Kirby, Catherine L.; Bragg, Debra D.; Taylor, Jason L.; Oertle, Kathleen M.

    2009-01-01

    This booklet provides information to multiple stakeholders on the implementation of career clusters in Illinois. The booklet is an extension of the previous edition titled "An Introduction to Illinois CTE Programs of Study" (2008), and provides a resource for partners to understand Illinois' Career Cluster Model as its own adaptation of the…

  1. Calixarene-supported clusters

    Taylor, Stephanie M.; McIntosh, Ruaraidh D.; Piligkos, Stergios; Dalgarno, Scott J.; Brechin, Euan K.

    2012-01-01

    A combination of complementary cluster ligands results in the formation of a new calixarene-supported ferromagnetic [Mn(5)] cage that displays the characteristic bonding modes of each support.......A combination of complementary cluster ligands results in the formation of a new calixarene-supported ferromagnetic [Mn(5)] cage that displays the characteristic bonding modes of each support....

  2. Brightest Cluster Galaxy Identification

    Leisman, Luke; Haarsma, D. B.; Sebald, D. A.; ACCEPT Team

    2011-01-01

    Brightest cluster galaxies (BCGs) play an important role in several fields of astronomical research. The literature includes many different methods and criteria for identifying the BCG in the cluster, such as choosing the brightest galaxy, the galaxy nearest the X-ray peak, or the galaxy with the most extended profile. Here we examine a sample of 75 clusters from the Archive of Chandra Cluster Entropy Profile Tables (ACCEPT) and the Sloan Digital Sky Survey (SDSS), measuring masked magnitudes and profiles for BCG candidates in each cluster. We first identified galaxies by hand; in 15% of clusters at least one team member selected a different galaxy than the others.We also applied 6 other identification methods to the ACCEPT sample; in 30% of clusters at least one of these methods selected a different galaxy than the other methods. We then developed an algorithm that weighs brightness, profile, and proximity to the X-ray peak and centroid. This algorithm incorporates the advantages of by-hand identification (weighing multiple properties) and automated selection (repeatable and consistent). The BCG population chosen by the algorithm is more uniform in its properties than populations selected by other methods, particularly in the relation between absolute magnitude (a proxy for galaxy mass) and average gas temperature (a proxy for cluster mass). This work supported by a Barry M. Goldwater Scholarship and a Sid Jansma Summer Research Fellowship.

  3. Management of cluster headache

    Tfelt-Hansen, Peer C; Jensen, Rigmor H

    2012-01-01

    should be checked with an ECG. At the start of a cluster, transitional preventive treatment such as corticosteroids or greater occipital nerve blockade can be given. In CCH and in long-standing clusters of ECH, lithium, methysergide, topiramate, valproic acid and ergotamine tartrate can be used as add...

  4. Blue emitting undecaplatinum clusters

    Chakraborty, Indranath; Bhuin, Radha Gobinda; Bhat, Shridevi; Pradeep, T.

    2014-07-01

    A blue luminescent 11-atom platinum cluster showing step-like optical features and the absence of plasmon absorption was synthesized. The cluster was purified using high performance liquid chromatography (HPLC). Electrospray ionization (ESI) and matrix assisted laser desorption ionization (MALDI) mass spectrometry (MS) suggest a composition, Pt11(BBS)8, which was confirmed by a range of other experimental tools. The cluster is highly stable and compatible with many organic solvents.A blue luminescent 11-atom platinum cluster showing step-like optical features and the absence of plasmon absorption was synthesized. The cluster was purified using high performance liquid chromatography (HPLC). Electrospray ionization (ESI) and matrix assisted laser desorption ionization (MALDI) mass spectrometry (MS) suggest a composition, Pt11(BBS)8, which was confirmed by a range of other experimental tools. The cluster is highly stable and compatible with many organic solvents. Electronic supplementary information (ESI) available: Details of experimental procedures, instrumentation, chromatogram of the crude cluster; SEM/EDAX, DLS, PXRD, TEM, FT-IR, and XPS of the isolated Pt11 cluster; UV/Vis, MALDI MS and SEM/EDAX of isolated 2 and 3; and 195Pt NMR of the K2PtCl6 standard. See DOI: 10.1039/c4nr02778g

  5. Melting of graphene clusters

    Singh, Sandeep Kumar; Neek-Amal, M.; Peeters, F. M.

    2013-01-01

    Density-functional tight-binding and classical molecular dynamics simulations are used to investigate the structural deformations and melting of planar carbon nano-clusters $C_{N}$ with N=2-55. The minimum energy configurations for different clusters are used as starting configuration for the study of the temperature effects on the bond breaking/rotation in carbon lines (N$

  6. Cold cluster ferromagnetism

    Bertsch, G.F. [Washington Univ., Seattle, WA (United States). Inst. for Nuclear Theory; Yabana, K. [Niigata Univ. (Japan). Dept. of Physics

    1993-12-31

    We examine the magnetic moment distribution of ferromagnetic clusters under conditions where the magnetic moment is aligned with the internal cluster axis. Analytic expressions are obtained for the moment distribution and the adiabatic average moment induced in low fields. The result differs from the low-field Langevin function by a factor 2/3.

  7. Investigation of Cluster and Cluster Queuing System

    Halifu, Saerda

    2008-01-01

    Cluster became main platform as parallel and distributed computing structure for high performance computing. Following the development of high performance computer architecture more and more different branches of natural science benefit fromhuge and efficient computational power. For instance bio-informatics, climate science, computational physics, computational chemistry, marine science, etc. Efficient and reliable computing powermay not only expending demand of existing high performance com...

  8. Job Oriented Monitoring Clusters

    Vijayalaxmi Cigala,

    2011-03-01

    Full Text Available There has been a lot of development in the field of clusters and grids. Recently, the use of clusters has been on rise in every possible field. This paper proposes a system that monitors jobs onlarge computational clusters. Monitoring jobs is essential to understand how jobs are being executed. This helps us in understanding the complete life cycle of the jobs being executed on large clusters. Also, this paper describes how the information obtained by monitoring the jobs would help in increasing the overall throughput of clusters. Heuristics help in efficient job distribution among the computational nodes, thereby accomplishing fair job distribution policy. The proposed system would be capable of loadbalancing among the computational nodes, detecting failures, taking corrective actions after failure detection, job monitoring, system resource monitoring, etc.

  9. Cool Cluster Correctly Correlated

    Sergey Aleksandrovich Varganov

    2005-12-17

    Atomic clusters are unique objects, which occupy an intermediate position between atoms and condensed matter systems. For a long time it was thought that physical and chemical properties of atomic dusters monotonically change with increasing size of the cluster from a single atom to a condensed matter system. However, recently it has become clear that many properties of atomic clusters can change drastically with the size of the clusters. Because physical and chemical properties of clusters can be adjusted simply by changing the cluster's size, different applications of atomic clusters were proposed. One example is the catalytic activity of clusters of specific sizes in different chemical reactions. Another example is a potential application of atomic clusters in microelectronics, where their band gaps can be adjusted by simply changing cluster sizes. In recent years significant advances in experimental techniques allow one to synthesize and study atomic clusters of specified sizes. However, the interpretation of the results is often difficult. The theoretical methods are frequently used to help in interpretation of complex experimental data. Most of the theoretical approaches have been based on empirical or semiempirical methods. These methods allow one to study large and small dusters using the same approximations. However, since empirical and semiempirical methods rely on simple models with many parameters, it is often difficult to estimate the quantitative and even qualitative accuracy of the results. On the other hand, because of significant advances in quantum chemical methods and computer capabilities, it is now possible to do high quality ab-initio calculations not only on systems of few atoms but on clusters of practical interest as well. In addition to accurate results for specific clusters, such methods can be used for benchmarking of different empirical and semiempirical approaches. The atomic clusters studied in this work contain from a few atoms

  10. Pulsars in Globular Clusters

    Camilo, F; Camilo, Fernando; Rasio, Frederic A.

    2005-01-01

    More than 100 radio pulsars have been detected in 24 globular clusters. The largest observed samples are in Terzan 5 and 47 Tucanae, which together contain 45 pulsars. Accurate timing solutions, including positions in the cluster, are known for many of these pulsars. Here we provide an observational overview of some properties of pulsars in globular clusters, as well as properties of the globular clusters with detected pulsars. The many recent detections also provide a new opportunity to re-examine theoretically the formation and evolution of recycled pulsars in globular clusters. Our brief review considers the most important dynamical interaction and binary evolution processes: collisions, exchange interactions, mass transfer, and common-envelope phases.

  11. Cluster ion beam evaporation

    Cluster ions can be made by the supercooling due to adiabatic expansion of substances to be vaporized which are ejected from a nozzle. This paper is described on the recent progress of studies concerning the cluster beam. The technique of cluster ion beam has been applied for the studies of thermonuclear plasma, the fabrication of thin films, crystal growth and electronic devices. The density of cluster ion beam is larger than that of atomic ion beam, and the formation of thin films can be easily done in high vacuum. This method is also useful for epitaxial growth. Metallic vapour cluster beam was made by the help of jetting rare gas beam. Various beam sources were developed. The characteristics of these sources were measured and analyzed. (Kato, T.)

  12. On TPC cluster reconstruction

    Dydak, F; Nefedov, Y; Wotschack, J; Zhemchugov, A

    2004-01-01

    For a bias-free momentum measurement of TPC tracks, the correct determination of cluster positions is mandatory. We argue in particular that (i) the reconstruction of the entire longitudinal signal shape in view of longitudinal diffusion, electronic pulse shaping, and track inclination is important both for the polar angle reconstruction and for optimum r phi resolution; and that (ii) self-crosstalk of pad signals calls for special measures for the reconstruction of the z coordinate. The problem of 'shadow clusters' is resolved. Algorithms are presented for accepting clusters as 'good' clusters, and for the reconstruction of the r phi and z cluster coordinates, including provisions for 'bad' pads and pads next to sector boundaries, respectively.

  13. Mathematical classification and clustering

    Mirkin, Boris

    1996-01-01

    I am very happy to have this opportunity to present the work of Boris Mirkin, a distinguished Russian scholar in the areas of data analysis and decision making methodologies. The monograph is devoted entirely to clustering, a discipline dispersed through many theoretical and application areas, from mathematical statistics and combina­ torial optimization to biology, sociology and organizational structures. It compiles an immense amount of research done to date, including many original Russian de­ velopments never presented to the international community before (for instance, cluster-by-cluster versions of the K-Means method in Chapter 4 or uniform par­ titioning in Chapter 5). The author's approach, approximation clustering, allows him both to systematize a great part of the discipline and to develop many in­ novative methods in the framework of optimization problems. The optimization methods considered are proved to be meaningful in the contexts of data analysis and clustering. The material presented in ...

  14. Improved Suffix Tree Clustering for Efficient Document Clustering

    Sonia Bansal; Niranjan Kumar

    2010-01-01

    Document clustering is a technology that puts pages into groups and is useful for categorizing, organizing, and refining search results. When clustering using only documents, Suffix Tree Clustering (STC) outperforms other clustering algorithms by making use of phrases and allowing clusters to overlap. STC is a linear time clustering which is based on identifying phrases that are common to groups of documents. STC treats a document as a string, making use of proximity information between words...

  15. Flexible Word Classes

    van Lier, Eva; Rijkhoff, Jan

    2013-01-01

    • First major publication on the phenomenon • Offers cross-linguistic, descriptive, and diverse theoretical approaches • Includes analysis of data from different language families and from lesser studied languages This book is the first major cross-linguistic study of 'flexible words', i.e. words...... that cannot be classified in terms of the traditional lexical categories Verb, Noun, Adjective or Adverb. Flexible words can - without special morphosyntactic marking - serve in functions for which other languages must employ members of two or more of the four traditional, 'specialised' word classes....... Thus, flexible words are underspecified for communicative functions like 'predicating' (verbal function), 'referring' (nominal function) or 'modifying' (a function typically associated with adjectives and e.g. manner adverbs). Even though linguists have been aware of flexible world classes for more...

  16. Clustering Hiérarchique de données à base de Ward.

    Tabet aoul, Walid Houcine

    2014-01-01

    Le clustering est considéré comme l’une des facultés facinantes du cerveau humain,plusieurs implémentations artificielles ,sont proposées pour simuler ce processus,nous distinguons le clustering à base de centroides,le clustering hiérarchique,le clustering à base de distibution…etc. Dans ce travail nous avons choisi la classe de clustering hiérarchique , et en particulier nous avons conçu et implémenter l’algorithme ascendant de Ward.Ce dernier possède beaucoup d’avantages, par rapport à d...

  17. Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture

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

    2009-12-22

    Document clustering methods, document cluster label disambiguation methods, document clustering apparatuses, and articles of manufacture are described. In one aspect, a document clustering method includes providing a document set comprising a plurality of documents, providing a cluster comprising a subset of the documents of the document set, using a plurality of terms of the documents, providing a cluster label indicative of subject matter content of the documents of the cluster, wherein the cluster label comprises a plurality of word senses, and selecting one of the word senses of the cluster label.

  18. Textile Industrial Clusters in China

    2010-01-01

    "National Textile Industry Cluster Development Seminar" convened, held by China National Textile and Apparel Council, 23 cities and towns were awarded as China’s Textile Industry Cluster Pilot District. By far, China’s textile industrial clusters have grown

  19. Spanning Tree Based Attribute Clustering

    Zeng, Yifeng; Jorge, Cordero Hernandez

    2009-01-01

    inconsistent edges from a maximum spanning tree by starting appropriate initial modes, therefore generating stable clusters. It discovers sound clusters through simple graph operations and achieves significant computational savings. We compare the Star Discovery algorithm against earlier attribute clustering...

  20. An Improved Fuzzy c-Means Clustering Algorithm Based on Shadowed Sets and PSO

    Jian Zhang

    2014-01-01

    Full Text Available To organize the wide variety of data sets automatically and acquire accurate classification, this paper presents a modified fuzzy c-means algorithm (SP-FCM based on particle swarm optimization (PSO and shadowed sets to perform feature clustering. SP-FCM introduces the global search property of PSO to deal with the problem of premature convergence of conventional fuzzy clustering, utilizes vagueness balance property of shadowed sets to handle overlapping among clusters, and models uncertainty in class boundaries. This new method uses Xie-Beni index as cluster validity and automatically finds the optimal cluster number within a specific range with cluster partitions that provide compact and well-separated clusters. Experiments show that the proposed approach significantly improves the clustering effect.

  1. Multiple populations in the Sagittarius nuclear cluster M 54 and in other anomalous globular clusters

    Milone, A. P.

    2016-08-01

    M 54 is the central cluster of the Sagittarius dwarf galaxy. This stellar system is now in process of being disrupted by the tidal interaction with the Milky Way and represents one of the building blocks of the Galactic Halo. Recent discoveries, based on the synergy of photometry and spectroscopy have revealed that the color-magnitude diagram (CMD) of some massive, anomalous, Globular Clusters (GCs) host stellar populations with different content of heavy elements. In this paper, I use multi-wavelength Hubble Space Telescope (HST) photometry to detect and characterize multiple stellar populations in M 54. I provide empirical evidence that this GC shares photometric and spectroscopic similarities with the class of anomalous GCs. These findings make it tempting to speculate that, similarly to Sagittarius nuclear cluster M 54, other anomalous GCs were born in an extra-Galactic environment.

  2. Multiple populations in the Sagittarius nuclear cluster M54 and in other anomalous globular clusters

    Milone, A P

    2015-01-01

    M54 is the central cluster of the Sagittarius dwarf galaxy. This stellar system is now in process of being disrupted by the tidal interaction with the Milky Way and represents one of the building blocks of the Galactic Halo. Recent discoveries, based on the synergy of photometry and spectroscopy have revealed that the color-magnitude diagram of some massive, anomalous, Globular Clusters (GCs) host stellar populations with different content of heavy elements.In this paper, I use multi-wavelength Hubble Space Telescope photometry to detect and characterize multiple stellar populations in M54. I provide empirical evidence that this GC shares photometric and spectroscopic similarities with the class of anomalous GCs. These findings make it tempting to speculate that, similarly to Sagittarius nuclear cluster M54, other anomalous GCs were born in an extra-Galactic environment.

  3. Attack Detection By Clustering And Classification Approach

    Priyanka J. Pathak, Prof. Snehlata Dongre

    2012-04-01

    Full Text Available Intrusion detection is a software application that monitors network and/or system activities for malicious activities or policy violations and produces reports to a Management Station. Security is becoming big issue for all networks. Hackers and intruders have made many successful attempts to bring down high profile company networks and web services. Intrusion Detection System (IDS is an important detection that is used as a countermeasure to preserve data integrity and system availability from attacks. The work is implemented in two phases, in first phase clustering by K-means is done and in next step of classification is done with k-nearest neighbours and decision trees. The objects are clustered or grouped based on the principle of maximizing the intra-class similarity and minimizing the interclass similarity. This paper proposes an approach which make the clusters of similar attacks and in next step of classification with K nearest neighbours and Decision trees it detect the attack types. This method is advantageous over single classifier as it detect better class than single classifier system.

  4. Software-Defined Cluster

    聂华; 杨晓君; 刘淘英

    2015-01-01

    The cluster architecture has played an important role in high-end computing for the past 20 years. With the advent of Internet services, big data, and cloud computing, traditional clusters face three challenges: 1) providing flexible system balance among computing, memory, and I/O capabilities;2) reducing resource pooling overheads;and 3) addressing low performance-power efficiency. This position paper proposes a software-defined cluster (SDC) architecture to deal with these challenges. The SDC architecture inherits two features of traditional cluster: its architecture is multicomputer and it has loosely-coupled interconnect. SDC provides two new mechanisms: global I/O space (GIO) and hardware-supported native access (HNA) to remote devices. Application software can define a virtual cluster best suited to its needs from resources pools provided by a physical cluster, and traditional cluster ecosystems need no modification. We also discuss a prototype design and implementation of a 32-processor cloud server utilizing the SDC architecture.

  5. Cluster knockout reactions

    Arun K Jain; B N Joshi

    2014-04-01

    Cluster knockout reactions are expected to reveal the amount of clustering (such as that of , d and even of heavier clusters such as 12C, 16O etc.) in the target nucleus. In simple terms, incident medium high-energy nuclear projectile interacts strongly with the cluster (present in the target nucleus) as if it were existing as a free entity. Theoretically, the relatively softer interactions of the two outgoing particles with the residual nucleus lead to optical distortions and are treated in terms of distorted wave (DW) formalism. The long-range projectile–cluster interaction is accounted for, in terms of the finite range (FR) direct reaction formalism, as against the more commonly adopted zero-range (ZR) distorted wave impulse approximation (DWIA) formalism. Comparison of the DWIA calculations with the observed data provide information about the momentum distribution and the clustering spectroscopic factor of the target nucleus. Interesting results and some recent advancements in the area of (, 2) reactions and heavy cluster knockout reactions are discussed. Importance of the finite-range vertex and the final-state interactions are brought out.

  6. Introduction to cluster dynamics

    Reinhard, Paul-Gerhard

    2008-01-01

    Clusters as mesoscopic particles represent an intermediate state of matter between single atoms and solid material. The tendency to miniaturise technical objects requires knowledge about systems which contain a ""small"" number of atoms or molecules only. This is all the more true for dynamical aspects, particularly in relation to the qick development of laser technology and femtosecond spectroscopy. Here, for the first time is a highly qualitative introduction to cluster physics. With its emphasis on cluster dynamics, this will be vital to everyone involved in this interdisciplinary subje

  7. Raspberry Pi super cluster

    Dennis, Andrew K

    2013-01-01

    This book follows a step-by-step, tutorial-based approach which will teach you how to develop your own super cluster using Raspberry Pi computers quickly and efficiently.Raspberry Pi Super Cluster is an introductory guide for those interested in experimenting with parallel computing at home. Aimed at Raspberry Pi enthusiasts, this book is a primer for getting your first cluster up and running.Basic knowledge of C or Java would be helpful but no prior knowledge of parallel computing is necessary.

  8. Partially supervised speaker clustering.

    Tang, Hao; Chu, Stephen Mingyu; Hasegawa-Johnson, Mark; Huang, Thomas S

    2012-05-01

    Content-based multimedia indexing, retrieval, and processing as well as multimedia databases demand the structuring of the media content (image, audio, video, text, etc.), one significant goal being to associate the identity of the content to the individual segments of the signals. In this paper, we specifically address the problem of speaker clustering, the task of assigning every speech utterance in an audio stream to its speaker. We offer a complete treatment to the idea of partially supervised speaker clustering, which refers to the use of our prior knowledge of speakers in general to assist the unsupervised speaker clustering process. By means of an independent training data set, we encode the prior knowledge at the various stages of the speaker clustering pipeline via 1) learning a speaker-discriminative acoustic feature transformation, 2) learning a universal speaker prior model, and 3) learning a discriminative speaker subspace, or equivalently, a speaker-discriminative distance metric. We study the directional scattering property of the Gaussian mixture model (GMM) mean supervector representation of utterances in the high-dimensional space, and advocate exploiting this property by using the cosine distance metric instead of the euclidean distance metric for speaker clustering in the GMM mean supervector space. We propose to perform discriminant analysis based on the cosine distance metric, which leads to a novel distance metric learning algorithm—linear spherical discriminant analysis (LSDA). We show that the proposed LSDA formulation can be systematically solved within the elegant graph embedding general dimensionality reduction framework. Our speaker clustering experiments on the GALE database clearly indicate that 1) our speaker clustering methods based on the GMM mean supervector representation and vector-based distance metrics outperform traditional speaker clustering methods based on the “bag of acoustic features” representation and statistical

  9. Dwarfs in Coma Cluster

    2007-01-01

    [figure removed for brevity, see original site] Click on image for larger poster version This false-color mosaic of the central region of the Coma cluster combines infrared and visible-light images to reveal thousands of faint objects (green). Follow-up observations showed that many of these objects, which appear here as faint green smudges, are dwarf galaxies belonging to the cluster. Two large elliptical galaxies, NGC 4889 and NGC 4874, dominate the cluster's center. The mosaic combines visible-light data from the Sloan Digital Sky Survey (color coded blue) with long- and short-wavelength infrared views (red and green, respectively) from NASA's Spitzer Space Telescope.

  10. Clustering in nuclear environment

    The properties of few-body clusters (mass number A ≤ 4) are modified if they are immersed in a nuclear medium. In particular, Pauli blocking that reflects the antisymmetrization of the many-body wave function is responsible for the medium modification of light clusters and the dissolution with increasing density. A more consistent description is given with takes also the contribution of correlations in the continuum into account. The relation between cluster formation in warm dense matter and in nuclear structure is discussed

  11. Semi-Supervised Clustering for High-Dimensional and Sparse Features

    Yan, Su

    2010-01-01

    Clustering is one of the most common data mining tasks, used frequently for data organization and analysis in various application domains. Traditional machine learning approaches to clustering are fully automated and unsupervised where class labels are unknown a priori. In real application domains, however, some "weak" form of side…

  12. The "p"-Median Model as a Tool for Clustering Psychological Data

    Kohn, Hans-Friedrich; Steinley, Douglas; Brusco, Michael J.

    2010-01-01

    The "p"-median clustering model represents a combinatorial approach to partition data sets into disjoint, nonhierarchical groups. Object classes are constructed around "exemplars", that is, manifest objects in the data set, with the remaining instances assigned to their closest cluster centers. Effective, state-of-the-art implementations of…

  13. Class Action and Class Settlement in a European Perspective

    Werlauff, Erik

    2013-01-01

    The article analyses the options for introducing common European rules on class action lawsuits with an opt-out-model in individual cases. An analysis is made of how the risks of misuse of class actions can be prevented. The article considers the Dutch rules on class settlements (the WCAM procedure...

  14. GEANT4 distributed computing for compact clusters

    Harrawood, Brian P., E-mail: brian.harrawood@duke.edu; Agasthya, Greeshma A.; Lakshmanan, Manu N.; Raterman, Gretchen; Kapadia, Anuj J.

    2014-11-11

    A new technique for distribution of GEANT4 processes is introduced to simplify running a simulation in a parallel environment such as a tightly coupled computer cluster. Using a new C++ class derived from the GEANT4 toolkit, multiple runs forming a single simulation are managed across a local network of computers with a simple inter-node communication protocol. The class is integrated with the GEANT4 toolkit and is designed to scale from a single symmetric multiprocessing (SMP) machine to compact clusters ranging in size from tens to thousands of nodes. User designed ‘work tickets’ are distributed to clients using a client–server work flow model to specify the parameters for each individual run of the simulation. The new g4DistributedRunManager class was developed and well tested in the course of our Neutron Stimulated Emission Computed Tomography (NSECT) experiments. It will be useful for anyone running GEANT4 for large discrete data sets such as covering a range of angles in computed tomography, calculating dose delivery with multiple fractions or simply speeding the through-put of a single model.

  15. GEANT4 distributed computing for compact clusters

    A new technique for distribution of GEANT4 processes is introduced to simplify running a simulation in a parallel environment such as a tightly coupled computer cluster. Using a new C++ class derived from the GEANT4 toolkit, multiple runs forming a single simulation are managed across a local network of computers with a simple inter-node communication protocol. The class is integrated with the GEANT4 toolkit and is designed to scale from a single symmetric multiprocessing (SMP) machine to compact clusters ranging in size from tens to thousands of nodes. User designed ‘work tickets’ are distributed to clients using a client–server work flow model to specify the parameters for each individual run of the simulation. The new g4DistributedRunManager class was developed and well tested in the course of our Neutron Stimulated Emission Computed Tomography (NSECT) experiments. It will be useful for anyone running GEANT4 for large discrete data sets such as covering a range of angles in computed tomography, calculating dose delivery with multiple fractions or simply speeding the through-put of a single model

  16. Packing of protein structures in clusters with magic numbers

    Lindgård, Per-Anker; Bohr, Henrik

    1997-01-01

    Recently we have proposed a model for folding proteins into packed `clusters'. We have constructed a local homology measure for protein fold classes by projecting consecutively secondary structures onto a lattice. Taking into account hydrophobic forces we have found a mechanism for formation of...... abundances. In this paper we demonstrate that the results are robust to variations in the coordination number of the model....

  17. Analysis & Prediction of Sales Data in SAP-ERP System using Clustering Algorithms

    Sastry, S. Hanumanth; Babu, Prof. M. S. Prasada

    2013-01-01

    Clustering is an important data mining technique where we will be interested in maximizing intracluster distance and also minimizing intercluster distance. We have utilized clustering techniques for detecting deviation in product sales and also to identify and compare sales over a particular period of time. Clustering is suited to group items that seem to fall naturally together, when there is no specified class for any new item. We have utilizedannual sales data of a steel major to analyze S...

  18. On the Timing Analysis of Cluster based Communication Devices for Large Scale Computing Systems

    Mohammed Mahfooz Sheikh; Khan, A.M.; U.N.Sinha

    2012-01-01

    Many parallel computing environments utilize cluster based architecture for large scale computing owing to the ease of their availability. As the cluster based approach may be used extensively,the interconnection mechanism plays a vital role in the performance of the system. The globally coupled class of problem is generally not amenable with the cluster based approach due to its substantial demand for communication across the architecture. In this paper we present a timing analysis of standa...

  19. A hypergraph-based model for graph clustering: application to image indexing

    Jouili, Salim; Tabbone, Salvatore

    2009-01-01

    Version finale disponible : www.springerlink.com International audience In this paper, we introduce a prototype-based clustering algorithm dealing with graphs. We propose a hypergraph-based model for graph data sets by allowing clusters overlapping. More precisely, in this representation one graph can be assigned to more than one cluster. Using the concept of the graph median and a given threshold, the proposed algorithm detects automatically the number of classes in the graph database....

  20. Comparison three methods of clustering: k-means, spectral clustering and hierarchical clustering

    Kowsari, Kamran

    2013-01-01

    Comparison of three kind of the clustering and find cost function and loss function and calculate them. Error rate of the clustering methods and how to calculate the error percentage always be one on the important factor for evaluating the clustering methods, so this paper introduce one way to calculate the error rate of clustering methods. Clustering algorithms can be divided into several categories including partitioning clustering algorithms, hierarchical algorithms and density based algor...

  1. GBM 489 Complete Class

    admn

    2015-01-01

    GBM 489 Complete Class Check this A+ tutorial guideline at   http://www.assignmentcloud.com/GBM-489/GBM-489-Strategic-Topics-in-Global-Business-Management GBM 489 Strategic Topics in Global Business Management GBM 489 Week 1 DQ 1 GBM 489 Week 1 DQ 2 GBM 489 Week 1 DQ 3 GBM 489 Week 1 Individual Global Trends Paper GBM 489 Week 2 DQ 1 GBM 489 Week 2 DQ 2 GBM 489 Week 2 DQ 3 GBM 489 Week 2 Individual Business Plan Article Analysis GBM 489...

  2. The Class of '34

    Cairney, Richard

    1995-01-01

    The Great Depression raged, governments were beleaguered, the unemployment rate stood at 30%, scurvy stalked the poor and no one was immune to contagious diseases such as scarlet fever, polio and measles when the University of Alberta School of Medicine's Class of '34 graduated. For four alumni who recently gathered in Edmonton—Drs. Morley Hodgson, Melvin Gaudin, John McLurg and Edmund Cairns—their 60th-anniversary reunion was a time to recall the changes they have witnessed in medicine, incl...

  3. A Uniqueness Theorem for Clustering

    Zadeh, Reza Bosagh

    2012-01-01

    Despite the widespread use of Clustering, there is distressingly little general theory of clustering available. Questions like "What distinguishes a clustering of data from other data partitioning?", "Are there any principles governing all clustering paradigms?", "How should a user choose an appropriate clustering algorithm for a particular task?", etc. are almost completely unanswered by the existing body of clustering literature. We consider an axiomatic approach to the theory of Clustering. We adopt the framework of Kleinberg, [Kle03]. By relaxing one of Kleinberg's clustering axioms, we sidestep his impossibility result and arrive at a consistent set of axioms. We suggest to extend these axioms, aiming to provide an axiomatic taxonomy of clustering paradigms. Such a taxonomy should provide users some guidance concerning the choice of the appropriate clustering paradigm for a given task. The main result of this paper is a set of abstract properties that characterize the Single-Linkage clustering function. ...

  4. Modes of clustered star formation

    Pfalzner, S; Olczak, C

    2012-01-01

    The realization that most stars form in clusters, raises the question of whether star/planet formation are influenced by the cluster environment. The stellar density in the most prevalent clusters is the key factor here. Whether dominant modes of clustered star formation exist is a fundamental question. Using near-neighbour searches in young clusters Bressert et al. (2010) claim this not to be the case and conclude that star formation is continuous from isolated to densely clustered. We investigate under which conditions near-neighbour searches can distinguish between different modes of clustered star formation. Near-neighbour searches are performed for model star clusters investigating the influence of the combination of different cluster modes, observational biases, and types of diagnostic and find that the cluster density profile, the relative sample sizes, limitations in observations and the choice of diagnostic method decides whether modelled modes of clustered star formation are detected. For centrally ...

  5. Clustering of Emerging Flux

    Ruzmaikin, A.

    1997-01-01

    Observations show that newly emerging flux tends to appear on the Solar surface at sites where there is flux already. This results in clustering of solar activity. Standard dynamo theories do not predict this effect.

  6. Dynamic Bayesian clustering.

    Fowler, Anna; Menon, Vilas; Heard, Nicholas A

    2013-10-01

    Clusters of time series data may change location and memberships over time; in gene expression data, this occurs as groups of genes or samples respond differently to stimuli or experimental conditions at different times. In order to uncover this underlying temporal structure, we consider dynamic clusters with time-dependent parameters which split and merge over time, enabling cluster memberships to change. These interesting time-dependent structures are useful in understanding the development of organisms or complex organs, and could not be identified using traditional clustering methods. In cell cycle data, these time-dependent structure may provide links between genes and stages of the cell cycle, whilst in developmental data sets they may highlight key developmental transitions. PMID:24131050

  7. Internal Cluster Structure

    Bartelmann, Matthias; Meneghetti, Massimo; Schmidt, Robert

    2013-01-01

    The core structure of galaxy clusters is fundamentally important. Even though self-gravitating systems have no stable equilibrium state due to their negative heat capacity, numerical simulations find density profiles which are universal in the sense that they are fairly flat within a scale radius and gradually steepen farther outward, asymptotically approaching a logarithmic slope of $\\approx-3$ near the virial radius. We argue that the reason for the formation of this profile is not satisfactorily understood. The ratio between the virial radius and the scale radius, the so-called concentration, is found in simulations to be closely related to the mass and the redshift and low for cluster-sized haloes, but observed to be substantially higher at least in a subset of observed clusters. Haloes formed from cold dark matter should furthermore be richly substructured. We review theoretical and observational aspects of cluster cores here, discuss modifications by baryonic physics and observables that can provide bet...

  8. Evolution of clustered storage

    CERN. Geneva; Van de Vyvre, Pierre

    2007-01-01

    The session actually featured two presentations: * Evolution of clustered storage by Lance Hukill, Quantum Corporation * ALICE DAQ - Usage of a Cluster-File System: Quantum StorNext by Pierre Vande Vyvre, CERN-PH the second one prepared at short notice by Pierre (thanks!) to present how the Quantum technologies are being used in the ALICE experiment. The abstract to Mr Hukill's follows. Clustered Storage is a technology that is driven by business and mission applications. The evolution of Clustered Storage solutions starts first at the alignment between End-users needs and Industry trends: * Push-and-Pull between managing for today versus planning for tomorrow * Breaking down the real business problems to the core applications * Commoditization of clients, servers, and target devices * Interchangeability, Interoperability, Remote Access, Centralized control * Oh, and yes, there is a budget and the "real world" to deal with This presentation will talk through these needs and trends, and then ask the question, ...

  9. Mining Java Class Naming Conventions

    Butler, Simon; Wermelinger, Michel; Yu, Yijun; Sharp, Helen

    2011-01-01

    Class names represent the concepts implemented in object-oriented source code and are key elements in program comprehension and, thus, software maintenance. Programming conventions often state that class names should be noun-phrases, but there is little further guidance for developers on the composition of class names. Other researchers have observed that the majority of Java class identifier names are composed of one or more nouns preceded, optionally, by one or more adjectives. However, no ...

  10. Electron microscopy studies of point defect clusters in metals

    Transmission electron microscopy (TEM) has been widely used to study point defect clusters in metals following quenching and irradiation. As a result of this work considerable progress has been made in our understanding of the nucleation morphology, and growth of clusters and this paper reviews the current state of this understanding. The cubic metals (fcc and bcc) have been studied most widely. Cluster geometry and how it is affected by a wide range of variables such as the temperature at which clustering occurs has been well characterized. Detailed quantitative studies have also been made of cluster nucleation and growth kinetics and a self-consistent picture has been developed of the physical processes involved which describes the behaviour of quenched-in and radiation-induced defects. It has been shown that fundamental differences exist between fcc and bcc metals particularly with regard to the formation of vacancy clusters. In contrast, comparatively little work has been carried out on hpc metals and a clear picture has not yet emerged of the factors influencing cluster geometry. The results indicate that considerable differences exist on going from one to another within the general class of hexagonal metals. Moreover, virtually no quantitative results have been reported on the nucleation and growth of clusters in hcp metals. (author)

  11. Cauchy cluster process

    Ghorbani, Mohammad

    2013-01-01

    In this paper we introduce an instance of the well-know Neyman–Scott cluster process model with clusters having a long tail behaviour. In our model the offspring points are distributed around the parent points according to a circular Cauchy distribution. Using a modified Cramér-von Misses test...... statistic and the simulated pointwise envelopes it is shown that this model fits better than the Thomas process to the frequently analyzed long-leaf pine data-set....

  12. Bayesian Nonparametric Graph Clustering

    Banerjee, Sayantan; Akbani, Rehan; Baladandayuthapani, Veerabhadran

    2015-01-01

    We present clustering methods for multivariate data exploiting the underlying geometry of the graphical structure between variables. As opposed to standard approaches that assume known graph structures, we first estimate the edge structure of the unknown graph using Bayesian neighborhood selection approaches, wherein we account for the uncertainty of graphical structure learning through model-averaged estimates of the suitable parameters. Subsequently, we develop a nonparametric graph cluster...

  13. Finnish Mobile Gaming Cluster

    Masira, Elijah; Chowdhury, Nafis Ahmed

    2014-01-01

    Abstract The Finnish mobile gaming cluster is one of the most promising industries that have been growing significantly in the past few years to become a substantial cultural export product / service of Finland. The main objective of this research was to gain a persuasive understanding about the emergence of the mobile gaming cluster in Finland and explore the factors behind its success. The literature review centers on M. E Porter’s publications on competitiveness and other publication...

  14. Industry clusters and SMEs

    Arnoud Muizer; Gert Jan Hospers

    1999-01-01

    Studie naar de rol van clusters van bedrijven in de economie. Clusters van bedrijven krijgen een groeiende aandacht op alle bestuurlijke niveaus. De achterliggende gedachte is dat samenwerking tussen bedrijven op technologisch gebied leidt tot de creatie van extra toegevoegde waarde, niet alleen voor de samenwerkingspartners zelf, maar ook voor de lokale, regionale en nationale economie. In de studie wordt een clusterdefinitie gepresenteerd en een raamwerk dat als basis kan dienen voor nader ...

  15. Clustering with topological constraints

    GRBEC, DEJAN

    2012-01-01

    In data analysis we often have to deal with object located somewhere in space. Data can be bound to cities, countries or other space object that have known spatial coordinates. This object can be clustered by selected attributes considering their adjacency. The object of diploma thesis was to develop procedures that consider objects neighboring relation. To determine the adjacency of object in space, we used the Voronoi diagram where the clusters of data should preset related parts of diagra...

  16. Hipax Cluster PACS Server

    Ramin Payrovi

    2007-01-01

    Best Performace: With our Hipax Cluster PACS Server solution we are introducing the parallel computing concept as an extremely fast software system to the PACS world. In contrast to the common PACS servers, the Hipax Cluster PACS software is not only restricted to one or two computers, but can be used on a couple of servers controlling each other."nThus, the same services can be run simultaneously on different computers. The scalable system can also be expanded subsequently without lost ...

  17. Clustering audiology data

    Anwar, Naveed; Oakes, Michael; Wermter, Stefan; Heinrich, Stefan

    2010-01-01

    In this paper we describe new results of statistical and neural data mining of audiology patient records, with the ultimate aim of looking for factors influencing which patients would most benefit from being fitted with a hearing aid. We describe how a combination of neural and statistical techniques can usefully subdivide a set of patients into clusters, based on their hearing thresholds at six different frequencies, and then label the clusters with meaningful text labels. In our first exper...

  18. Determination of atomic cluster structure with cluster fusion algorithm

    Obolensky, Oleg I.; Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters.......We report an efficient scheme of global optimization, called cluster fusion algorithm, which has proved its reliability and high efficiency in determination of the structure of various atomic clusters....

  19. Cluster fusion algorithm: application to Lennard-Jones clusters

    Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    2008-01-01

    We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing ...

  20. Cluster fusion algorithm: application to Lennard-Jones clusters

    Solov'yov, Ilia; Solov'yov, Andrey V.; Greiner, Walter

    2006-01-01

    We present a new general theoretical framework for modelling the cluster structure and apply it to description of the Lennard-Jones clusters. Starting from the initial tetrahedral cluster configuration, adding new atoms to the system and absorbing its energy at each step, we find cluster growing ...

  1. Sparse Subspace Clustering: Algorithm, Theory, and Applications

    Elhamifar, Ehsan

    2012-01-01

    In many real-world problems, we are dealing with collections of high-dimensional data, such as images, videos, text and web documents, DNA microarray data, and more. Often, high-dimensional data lie close to low-dimensional structures corresponding to several classes or categories of the data. In this paper, we propose and study an algorithm, called Sparse Subspace Clustering, to cluster data points that lie in a union of low-dimensional subspaces. The key idea is that, among infinitely many possible representations of a data point in terms of other points, a sparse representation corresponds to selecting a few points that come from the same subspace. This motivates solving a sparse optimization program whose solution is used in a spectral clustering framework to infer the clustering of the data into subspaces. As solving the sparse optimization program is NP-hard, we consider its convex relaxation and show that, under appropriate conditions on the arrangement of the subspaces and the distribution of the data...

  2. URBAN AND REGIONAL CREATIVE CLASS THEORIES

    Tremblay, Remy; CHICOINE, Huges

    2011-01-01

    The creative class theory suggests that society is composed of three social classes: the creative class, the service class, the working class, a different view or classification of the workforce. The boundaries separating the creative class from the upper ruling class and the lower working class rest on occupational premises. This paper examines the construction and tools of the creative class, its human composition as a social class, attending discourses, as well as the shifting foundations ...

  3. Cluster bomb ocular injuries

    Ahmad M Mansour

    2012-01-01

    Full Text Available Purpose: To present the visual outcomes and ocular sequelae of victims of cluster bombs. Materials and Methods: This retrospective, multicenter case series of ocular injury due to cluster bombs was conducted for 3 years after the war in South Lebanon (July 2006. Data were gathered from the reports to the Information Management System for Mine Action. Results: There were 308 victims of clusters bombs; 36 individuals were killed, of which 2 received ocular lacerations and; 272 individuals were injured with 18 receiving ocular injury. These 18 surviving individuals were assessed by the authors. Ocular injury occurred in 6.5% (20/308 of cluster bomb victims. Trauma to multiple organs occurred in 12 of 18 cases (67% with ocular injury. Ocular findings included corneal or scleral lacerations (16 eyes, corneal foreign bodies (9 eyes, corneal decompensation (2 eyes, ruptured cataract (6 eyes, and intravitreal foreign bodies (10 eyes. The corneas of one patient had extreme attenuation of the endothelium. Conclusions: Ocular injury occurred in 6.5% of cluster bomb victims and 67% of the patients with ocular injury sustained trauma to multiple organs. Visual morbidity in civilians is an additional reason for a global ban on the use of cluster bombs.

  4. Centroid Based Text Clustering

    Priti Maheshwari

    2010-09-01

    Full Text Available Web mining is a burgeoning new field that attempts to glean meaningful information from natural language text. Web mining refers generally to the process of extracting interesting information and knowledge from unstructured text. Text clustering is one of the important Web mining functionalities. Text clustering is the task in which texts are classified into groups of similar objects based on their contents. Current research in the area of Web mining is tacklesproblems of text data representation, classification, clustering, information extraction or the search for and modeling of hidden patterns. In this paper we propose for mining large document collections it is necessary to pre-process the web documents and store the information in a data structure, which is more appropriate for further processing than a plain web file. In this paper we developed a php-mySql based utility to convert unstructured web documents into structured tabular representation by preprocessing, indexing .We apply centroid based web clustering method on preprocessed data. We apply three methods for clustering. Finally we proposed a method that can increase accuracy based on clustering ofdocuments.

  5. Galaxy cluster's rotation

    Manolopoulou, Maria

    2016-01-01

    We study the possible rotation of cluster galaxies, developing, testing and applying a novel algorithm which identifies rotation, if such does exits, as well as its rotational centre, its axis orientation, rotational velocity amplitude and, finally, the clockwise or counterclockwise direction of rotation on the plane of the sky. To validate our algorithms we construct realistic Monte-Carlo mock rotating clusters and confirm that our method provides robust indications of rotation. We then apply our methodology on a sample of Abell clusters with z<~0.1 with member galaxies selected from the SDSS DR10 spectroscopic database. We find that ~35% of our clusters are rotating when using a set of strict criteria, while loosening the criteria we find this fraction increasing to ~48%. We correlate our rotation indicators with the cluster dynamical state, provided either by their Bautz-Morgan type or by their X-ray isophotal shape and find for those clusters showing rotation that the significance and strength of their...

  6. Teachers, Social Class and Underachievement

    Dunne, Mairead; Gazeley, Louise

    2008-01-01

    Addressing the "the social class attainment gap" in education has become a government priority in England. Despite multiple initiatives, however, little has effectively addressed the underachievement of working-class pupils within the classroom. In order to develop clearer understandings of working-class underachievement at this level, this small…

  7. Advanced Low Energy Adaptive Clustering Hierarchy

    Ezzati Abdellah,

    2010-10-01

    Full Text Available The use of Wireless Sensor Networks (WSNs is anticipated to bring enormous changes in data gathering, processing and dissemination for different environments and applications. However, a WSN is a power constrained system, since nodes run on limited power batteries which shorten its lifespan. Prolonging the network lifetime depends on efficient management of sensing node energy resource. Hierarchicalrouting protocols are best known in regard to energy efficiency. By using a clustering technique hierarchical routing protocols greatly minimize energy consumed in collecting and disseminating data. Low Energy Adaptive Clustering Hierarchy (LEACH is one of the undamental protocols in this class. In this paper we propose Advanced LEACH (A-LEACH, a heterogeneous-energy protocol to decrease probability of failure nodes and to prolong the time interval before the death of the first node (we refer to as stability period and increasing the lifetime in heterogeneous WSNs, which is crucial for many applications.

  8. Unsupervised learning of broad phonetic classes with a statistical mixture model

    Lin, Ying

    2001-05-01

    Unsupervised learning of broad phonetic classes by infants was simulated using a statistical mixture model. A mixture model assumes that data are generated by a certain number of different sources-in this case, broad phonetic classes. With the phonetic labels removed, hand-transcribed segments from the TIMIT database were used in model-based clustering to obtain data-driven classes. Simple hidden Markov models were chosen to be the components of the mixture, with mel-cepstral coefficients as the front end. The mixture model was trained using an expectation-maximization-like algorithm. The EM-like algorithm was initialized by a K-means procedure and then applied to estimate the parameters of the mixture model after iteratively partitioning the clusters. The results of running this algorithm on the TIMIT segments suggested that the partitions may be interpreted as gradient acoustic features, and that to some degree the resulting clusters correspond to knowledge-based phonetic classes. Although such correspondences are rather rough, a careful examination of the clusters showed that the class membership of some sounds is highly dependent on their phonetic contexts. Thus, the clusters may reflect the preliminary phonological categories formed during language learning in early childhood.

  9. Atlas-guided cluster analysis of large tractography datasets.

    Ros, Christian; Güllmar, Daniel; Stenzel, Martin; Mentzel, Hans-Joachim; Reichenbach, Jürgen Rainer

    2013-01-01

    Diffusion Tensor Imaging (DTI) and fiber tractography are important tools to map the cerebral white matter microstructure in vivo and to model the underlying axonal pathways in the brain with three-dimensional fiber tracts. As the fast and consistent extraction of anatomically correct fiber bundles for multiple datasets is still challenging, we present a novel atlas-guided clustering framework for exploratory data analysis of large tractography datasets. The framework uses an hierarchical cluster analysis approach that exploits the inherent redundancy in large datasets to time-efficiently group fiber tracts. Structural information of a white matter atlas can be incorporated into the clustering to achieve an anatomically correct and reproducible grouping of fiber tracts. This approach facilitates not only the identification of the bundles corresponding to the classes of the atlas; it also enables the extraction of bundles that are not present in the atlas. The new technique was applied to cluster datasets of 46 healthy subjects. Prospects of automatic and anatomically correct as well as reproducible clustering are explored. Reconstructed clusters were well separated and showed good correspondence to anatomical bundles. Using the atlas-guided cluster approach, we observed consistent results across subjects with high reproducibility. In order to investigate the outlier elimination performance of the clustering algorithm, scenarios with varying amounts of noise were simulated and clustered with three different outlier elimination strategies. By exploiting the multithreading capabilities of modern multiprocessor systems in combination with novel algorithms, our toolkit clusters large datasets in a couple of minutes. Experiments were conducted to investigate the achievable speedup and to demonstrate the high performance of the clustering framework in a multiprocessing environment. PMID:24386292

  10. Lab classes in chemistry learning an artificial intelligence view

    Figueiredo, Margarida; Esteves, M. Lurdes; Neves, José; Vicente, Henrique (Orientador)

    2014-01-01

    The teaching methodology used in lab classes in Chemistry Learning was studied for a cohort of 702 students in the 10th grade of Portuguese Secondary Schools. The k-Means Clustering Method, with k values ranging between 2 (two) and 4 (four), was used in order to segment the data. Decision Trees were used for the development of explanatory models of the segmentation. The results obtained showed that the majority of the answerers considered that experimentation is central on Chemistry learning....

  11. Discriminant non-stationary signal features' clustering using hard and fuzzy cluster labeling

    Ghoraani, Behnaz; Krishnan, Sridhar

    2012-12-01

    Current approaches to improve the pattern recognition performance mainly focus on either extracting non-stationary and discriminant features of each class, or employing complex and nonlinear feature classifiers. However, little attention has been paid to the integration of these two approaches. Combining non-stationary feature analysis with complex feature classifiers, this article presents a novel direction to enhance the discriminatory power of pattern recognition methods. This approach, which is based on a fusion of non-stationary feature analysis with clustering techniques, proposes an algorithm to adaptively identify the feature vectors according to their importance in representing the patterns of discrimination. Non-stationary feature vectors are extracted using a non-stationary method based on time-frequency distribution and non-negative matrix factorization. The clustering algorithms including the K-means and self-organizing tree maps are utilized as unsupervised clustering methods followed by a supervised labeling. Two labeling methods are introduced: hard and fuzzy labeling. The article covers in detail the formulation of the proposed discriminant feature clustering method. Experiments performed with pathological speech classification, T-wave alternans evaluation from the surface electrocardiogram, audio scene analysis, and telemonitoring of Parkinson's disease problems produced desirable results. The outcome demonstrates the benefits of non-stationary feature fusion with clustering methods for complex data analysis where existing approaches do not exhibit a high performance.

  12. Single-Seed Cascades on Clustered Networks

    McSweeney, John K

    2015-01-01

    We consider a dynamic network cascade process developed by Watts applied to a random networks with a specified amount of clustering, belonging to a class of random networks developed by Newman. We adapt existing tree-based methods to formulate an appropriate two-type branching process to describe the spread of a cascade started with a single active node, and obtain a fixed-point equation to implicitly express the extinction probability of such a cascade. In so doing, we also recover a special case of a formula of Hackett et al. giving conditions for certain extinction of the cascade.

  13. The rotation of Galaxy Clusters

    Tovmassian, Hrant M.

    2015-01-01

    The method for detection of the galaxy cluster rotation based on the study of distribution of member galaxies with velocities lower and higher of the cluster mean velocity over the cluster image is proposed. The search for rotation is made for flat clusters with $a/b>1.8$ and BMI type clusters which are expected to be rotating. For comparison there were studied also round clusters and clusters of NBMI type, the second by brightness galaxy in which does not differ significantly from the cluste...

  14. Cluster Implantation and Deposition Apparatus

    Hanif, Muhammad; Popok, Vladimir

    In the current report, a design and capabilities of a cluster implantation and deposition apparatus (CIDA) involving two different cluster sources are described. The clusters produced from gas precursors (Ar, N etc.) by PuCluS-2 can be used to study cluster ion implantation in order to develop...... contributions to the theory of cluster stopping in matter as well as for practical applications requiring ultra-shallow implantation and modification of surfaces on the nanoscale. Metal clusters from the magnetron cluster source are of interest for the production of optical sensors to detect specific biological...

  15. Stellar populations in star clusters

    Li, Chengyuan; Deng, Licai

    2016-01-01

    Stellar populations contain the most important information about star clus- ter formation and evolution. Until several decades ago, star clusters were believed to be ideal laboratories for studies of simple stellar populations (SSPs). However, discoveries of multiple stellar populations in Galactic globular clusters have expanded our view on stellar populations in star clusters. They have simultaneously generated a number of controversies, particularly as to whether young star clusters may have the same origin as old globular clusters. In addition, extensive studies have revealed that the SSP scenario does not seem to hold for some intermediate-age and young star clusters either, thus making the origin of multiple stellar populations in star clusters even more complicated. Stellar population anomalies in numerous star clusters are well-documented, implying that the notion of star clusters as true SSPs faces serious challenges. In this review, we focus on stellar populations in massive clusters with different ...

  16. Class Generation for Numerical Wind Atlases

    Cutler, N.J.; Jørgensen, B.H.; Ersbøll, Bjarne Kjær; Badger, J.

    2006-01-01

    A new optimised clustering method is presented for generating wind classes for mesoscale modelling to produce numerical wind atlases. It is compared with the existing method of dividing the data in 12 to 16 sectors, 3 to 7 wind-speed bins and dividing again according to the stability of the...... atmosphere. Wind atlases are typically produced using many years of on-site wind observations at many locations. Numerical wind atlases are the result of mesoscale model integrations based on synoptic scale wind climates and can be produced in a number of hours of computation. 40 years of twice daily NCEP...... optimising the representation of the data and by automating the procedure more. The Karlsruhe Atmospheric Mesoscale Model (KAMM) is combined with the WAsP analysis to produce numerical wind atlases for two sites, Ireland and Egypt. The model results are compared with wind atlases made from measurements at...

  17. Understanding Class in Contemporary Societies

    Harrits, Gitte Sommer

    In this paper, I argue that claims about the death of class and the coming of the classless society are premature. Such claims are seldom genuinely empirical, and the theoretical argument often refers to a simple and therefore easily dismissible concept of class. By rejecting the concept of class...... altogether, sociological theory runs the risk of loosing the capacity for analysing stratification and vertical differentiation of power and freedom, which in late modernity seem to be a of continuing importance. Hence, I argue that although class analysis faces a number of serious challenges, it is possible...... to reinvent class analysis. The sociology of Pierre Bourdieu in many ways introduces an appropriate paradigm, and the paper therefore critically discusses Bourdieu's concept of class. Since the "Bourdieuan" class concept is primarily epistemological, i.e. a research strategy more than a theory...

  18. Pseudo Class III malocclusion.

    Al-Hummayani, Fadia M

    2016-04-01

    The treatment of deep anterior crossbite is technically challenging due to the difficulty of placing traditional brackets with fixed appliances. This case report represents a none traditional treatment modality to treat deep anterior crossbite in an adult pseudo class III malocclusion complicated by severely retruded, supraerupted upper and lower incisors. Treatment was carried out in 2 phases. Phase I treatment was performed by removable appliance "modified Hawley appliance with inverted labial bow," some modifications were carried out to it to suit the presented case. Positive overbite and overjet was accomplished in one month, in this phase with minimal forces exerted on the lower incisors. Whereas, phase II treatment was performed with fixed appliances (braces) to align teeth and have proper over bite and overjet and to close posterior open bite, this phase was accomplished within 11 month. PMID:27052290

  19. Pseudo Class III malocclusion

    Al-Hummayani, Fadia M.

    2016-01-01

    The treatment of deep anterior crossbite is technically challenging due to the difficulty of placing traditional brackets with fixed appliances. This case report represents a none traditional treatment modality to treat deep anterior crossbite in an adult pseudo class III malocclusion complicated by severely retruded, supraerupted upper and lower incisors. Treatment was carried out in 2 phases. Phase I treatment was performed by removable appliance “modified Hawley appliance with inverted labial bow,” some modifications were carried out to it to suit the presented case. Positive overbite and overjet was accomplished in one month, in this phase with minimal forces exerted on the lower incisors. Whereas, phase II treatment was performed with fixed appliances (braces) to align teeth and have proper over bite and overjet and to close posterior open bite, this phase was accomplished within 11 month. PMID:27052290

  20. Clustering is Easy When ....What?

    Ben-David, Shai

    2015-01-01

    It is well known that most of the common clustering objectives are NP-hard to optimize. In practice, however, clustering is being routinely carried out. One approach for providing theoretical understanding of this seeming discrepancy is to come up with notions of clusterability that distinguish realistically interesting input data from worst-case data sets. The hope is that there will be clustering algorithms that are provably efficient on such "clusterable" instances. This paper addresses th...

  1. Splitting Methods for Convex Clustering

    Chi, Eric C.; Lange, Kenneth

    2013-01-01

    Clustering is a fundamental problem in many scientific applications. Standard methods such as $k$-means, Gaussian mixture models, and hierarchical clustering, however, are beset by local minima, which are sometimes drastically suboptimal. Recently introduced convex relaxations of $k$-means and hierarchical clustering shrink cluster centroids toward one another and ensure a unique global minimizer. In this work we present two splitting methods for solving the convex clustering problem. The fir...

  2. Bootstrap clustering for graph partitioning

    Gambette, Philippe; Guénoche, Alain

    2011-01-01

    Given a simple undirected weighted or unweighted graph, we try to cluster the vertex set into communities and also to quantify the robustness of these clusters. For that task, we propose a new method, called bootstrap clustering which consists in (i) defining a new clustering algorithm for graphs, (ii) building a set of graphs similar to the initial one, (iii) applying the clustering method to each of them, making a profile (set) of partitions, (iv) computing a consensus partition for this pr...

  3. Anticancer properties of distinct antimalarial drug classes.

    Rob Hooft van Huijsduijnen

    Full Text Available We have tested five distinct classes of established and experimental antimalarial drugs for their anticancer potential, using a panel of 91 human cancer lines. Three classes of drugs: artemisinins, synthetic peroxides and DHFR (dihydrofolate reductase inhibitors effected potent inhibition of proliferation with IC50s in the nM- low µM range, whereas a DHODH (dihydroorotate dehydrogenase and a putative kinase inhibitor displayed no activity. Furthermore, significant synergies were identified with erlotinib, imatinib, cisplatin, dasatinib and vincristine. Cluster analysis of the antimalarials based on their differential inhibition of the various cancer lines clearly segregated the synthetic peroxides OZ277 and OZ439 from the artemisinin cluster that included artesunate, dihydroartemisinin and artemisone, and from the DHFR inhibitors pyrimethamine and P218 (a parasite DHFR inhibitor, emphasizing their shared mode of action. In order to further understand the basis of the selectivity of these compounds against different cancers, microarray-based gene expression data for 85 of the used cell lines were generated. For each compound, distinct sets of genes were identified whose expression significantly correlated with compound sensitivity. Several of the antimalarials tested in this study have well-established and excellent safety profiles with a plasma exposure, when conservatively used in malaria, that is well above the IC50s that we identified in this study. Given their unique mode of action and potential for unique synergies with established anticancer drugs, our results provide a strong basis to further explore the potential application of these compounds in cancer in pre-clinical or and clinical settings.

  4. Age dating old globular clusters in early-type galaxies

    Kissler-Patig, M

    1999-01-01

    Various methods for age dating globular clusters in ellipticals are presented. We first present spectroscopy of individual globular clusters (feasible with the advent of the 10m-class telescopes), and the measurement of Balmer line indices. Second, we discuss the photometry of globular cluster sub-populations and the mean age determination by comparison with population synthesis models. The first method is time consuming but precise once spectra with high enough signal to noise are obtained. One caveat, however, is the definition of the Balmer line indices that often include metal features and are themselves not independent of metallicity. The second method requires the measurement of two photometric quantities that depend differently from age and metallicity. The combination of both allows to break the age-metallicity degeneracy present in broad-band colors. Near-infrared colors can strongly complement the optical studies in this respect. Overall it is, however, true that the older a star cluster, the harder...

  5. Fractal clusters and intermittency in relativistic heavy ion collisions

    Antoniou, Nikos G; Diakonos, F K

    2000-01-01

    We investigate the formation of particle clusters (pions) associated with the chiral QCD phase transition in thermodynamic equilibrium. The geometry of these clusters at the critical point (T=T/sub c/) turns out to be fractal reflecting the self similar structure of the density fluctuations of the produced pions. The fractal dimension of the "critical" clusters is related to the critical exponents characterizing the phase transition. We examine the possibility to observe such pion clusters in relativistic heavy ion collisions. A Monte Carlo simulation of the critical system in 3D is performed leading to a typical set of "critical" events. Factorial moment analysis of the rapidity and transverse momentum distribution for these events shows a characteristic intermittency pattern. Mini-jet like structures arise in a azimuth angle rapidity lego plot. These features allow for a unique identification of the class of "critical events" in an event by event analysis in current and future experiments with relativistic ...

  6. Achieving Order through CHAOS: the LLNL HPC Linux Cluster Experience

    Braby, R L; Garlick, J E; Goldstone, R J

    2003-05-02

    Since fall 2001, Livermore Computing at Lawrence Livermore National Laboratory has deployed 11 Intel IA-32-based Linux clusters ranging in size up to 1154 nodes. All provide a common programming model and implement a similar cluster architecture. Hardware components are carefully selected for performance, usability, manageability, and reliability and are then integrated and supported using a strategy that evolved from practical experience. Livermore Computing Linux clusters run a common software environment that is developed and maintained in-house while drawing components and additional support from the open source community and industrial partnerships. The environment is based on Red Hat Linux and adds kernel modifications, cluster system management, monitoring and failure detection, resource management, authentication and access control, development environment, and parallel file system. The overall strategy has been successful and demonstrates that world-class high-performance computing resources can be built and maintained using commodity off-the-shelf hardware and open source software.

  7. An Artificial Immune Classification and Clustering Systems: A Survey

    Stephen Ajay Anurag Beri

    2014-01-01

    Full Text Available Artificial immune systems (AIS are a class of computationally intelligent systems which consider many properties of natural immune system .Several AIS are widely used in different application areas such as classification, clustering, web mining, virus detection, learning, image processing, robotics control, bio-informatics and anomaly detection. Among this classification and clustering are widely used areas. Most of the the artificial immune system used in the classification and clustering area make use some key features of AIS such as feature extraction, recognition and learning. This paper gives an effective survey aboutartificial immune systems which are used in the classification and clustering areasand also make use of the features such as feature selection, pattern recognition and machine learning.

  8. Cluster-based Data Gathering in Long-Strip Wireless Sensor Networks

    FANG, W.

    2012-02-01

    Full Text Available This paper investigates a special class of wireless sensor networks that are different from traditional ones in that the sensor nodes in this class of networks are deployed along narrowly elongated geographical areas and form a long-strip topology. According to hardware capabilities of current sensor nodes, a cluster-based protocol for reliable and efficient data gathering in long-strip wireless sensor networks (LSWSN is proposed. A well-distributed cluster-based architecture is first formed in the whole network through contention-based cluster head election. Cluster heads are responsible for coordination among the nodes within their clusters and aggregation of their sensory data, as well as transmission the data to the sink node on behalf of their own clusters. The intra-cluster coordination is based on the traditional TDMA schedule, in which the inter-cluster interference caused by the border nodes is solved by the multi-channel communication technique. The cluster reporting is based on the CSMA contention, in which a connected overlay network is formed by relay nodes to forward the data from the cluster heads through multi-hops to the sink node. The relay nodes are non-uniformly deployed to resolve the energy-hole problem which is extremely serious in the LSWSN. Extensive simulation results illuminate the distinguished performance of the proposed protocol.

  9. Definition of supertypes for HLA molecules using clustering of specificity matrices

    Lund, Ole; Nielsen, Morten; Kesmir, Can;

    2004-01-01

    Major histocompatibility complex (MHC) proteins are encoded by extremely polymorphic genes and play a crucial role in immunity. However, not all genetically different MHC molecules are functionally different. Sette and Sidney (1999) have defined nine HLA class I supertypes and showed that with only...... specificities. We report that the previously observed specificities of these class II molecules can be clustered into nine classes, which only partly correspond to the serological classification. We show that classification of HLA molecules may be done in a uniform and automated way. The definition of clusters...

  10. Cage Clusters of Gold and Tin: Golden Buckyballs and Stannaspherene

    Wang, Lai-Sheng

    2008-03-01

    Photoelectron spectroscopy (PES) yields direct electronic structure information for size-selected clusters. Combining PES with theoretical calculations has become an effective approach to obtain structural information for small and medium-sized clusters. We present recent discoveries of two classes of cage clusters in gold and tin. Negatively charged gold clusters (Aun^-) have been shown to exhibit a remarkable structural diversity from 2D structures for n = 4-12 and the pyramidal structure for n = 20. Using PES and DFT calculations, we have found that gold clusters with n = 16-18 possess unprecedented hollow cage structures. We have been able to successfully dope a variety of transition-metal atoms into the empty spaces in the golden cages, confirming their structural robustness, as well as demonstrating chemical tuning of their electronic, magnetic, and catalytic properties. Unlike carbon, the heavier congeners of the group 14 elements are not known to form hollow cage structures similar to the fullerenes. In PES studies of tin clusters, we noted that the spectrum of Sn12^- is distinctly different from that of its neighbors or its Si/Ge counterpart. This observation led to our discovery of a highly symmetric and stable icosahedral Sn12^2- cage, for which we coined a name ``stannaspherene'' to describe its high symmetry and spherical pi bonding. We have also shown that all transition metals including the f-block elements can be doped inside Sn12^2- to form a whole class of endohedral stannaspherenes, which may be used as potential building blocks for new cluster-assembled materials. In a preliminary experiment to synthesize stannaspherene in the bulk, a new cluster, Pd2@Sn18^4-, was crystallized and characterized, suggesting all stannaspherene and endohedral stannasphernes may be fabricated in the bulk under suitable conditions.

  11. Kohonen Maps Combined to K-means in a Two Level Strategy for Time Series ClusteringApplication to Meteorological and Electricity Load data

    Tarek, Khadir M.; Sofiane, Khdairia; Farouk, Benabbas

    2010-01-01

    Time series analysis using Kohonen maps, allows a rough visual identification of the different existing classes. The K-Means algorithm comes as a complement for better class clustering and a clear frontiers definition when validated using different types of indices. Using a two stage clustering procedure seems to be more efficient than a direct clustering approach involving only SOM or K-means algorithms from an applicative and results view points. The obtained classification is also more com...

  12. Globular cluster winds

    Current evolutionary theory indicates that the evolving stars in globular clusters arrive on the horizontal branch with approx. 30% less mass than they had on the main sequence. If, as seems likely, this mass loss results from the outflow of unprocessed material at the stellar surface during the giant stage, and if the ejected mass were retained within the cluster during the giant stage, and if the ejected mass were retained within the cluster between successive sweeps through the galactic plane (about 108 yr), sufficient hydrogen (100-2000Msub(o)) should accumulate for detection. Radio searches have failed to find evidence for either neutral or ionized gas. The search was therefore extended into the optical region and the time independant gas flow models were calculated to resolve this anomaly. (R.L.)

  13. Ants for Document Clustering

    Priya Vaijayanthi

    2012-03-01

    Full Text Available The usage of computers for mass storage has become mandatory nowadays due to World Wide Web (WWW. This has placed many challenges to the Information Retrieval (IR system. Clustering of documents available improves the efficiency of IR system. The problem of clustering has become a combinatorial optimization problem in IR system due to the exponential growth in information over WWW. In this paper, a hybrid algorithm that combines the basic Ant Colony Optimization with Tabu search has been proposed. The feasibility of the proposed algorithm is tested over a few standard benchmark datasets. The experimental results reveal that the proposed algorithm yields promising quality clusters compared to other ones produced by K-means algorithm.

  14. Clustering Game Behavior Data

    Bauckhage, C.; Drachen, Anders; Sifa, Rafet

    2015-01-01

    data scientists and present a review and tutorial focusing on the application of clustering techniques to mine behavioral game data. Several algorithms are reviewed and examples of their application shown. Key topics such as feature normalization are discussed and open problems in the context of game......Recent years have seen a deluge of behavioral data from players hitting the game industry. Reasons for this data surge are many and include the introduction of new business models, technical innovations, the popularity of online games, and the increasing persistence of games. Irrespective of the...... causes, the proliferation of behavioral data poses the problem of how to derive insights therefrom. Behavioral data sets can be large, time-dependent and high-dimensional. Clustering offers a way to explore such data and to discover patterns that can reduce the overall complexity of the data. Clustering...

  15. Spanish clitic clusters

    María Cristina Cuervo

    2013-11-01

    Full Text Available This paper deals with a small set of data from clusters of three clitics in Spanish that questions the empirical adequacy and scope of previous analyses of clitic clusters in Romance. It is shown that the output of the Spurious se Rule is not identical to genuine se, at some level that is relevant for linearization of clitics within a cluster. A proposal is presented to capture the neglected data, and this is done in a way that illuminates the debate on the division of labour in clitic phenomena between phonology, morphology and syntax. Central questions in morphology, such as ordering of operations, syncretisms, linearization principles and consequences of lexical insertion are addressed and re-examined.

  16. Detection and Analysis of Clones in UML Class Models

    Dhavleesh Rattan

    2015-07-01

    Full Text Available It is quite frequent to copy and paste code fragments in software development. The copied source code is called a software clone and the activity is referred to as code cloning. The presence of code clones hamper maintenance and may lead to bug propagation. Now-a-days, model driven development has become a standard industry practice. Duplicate parts in models i.e. model clones pose similar challenges as in source code. This paper presents an approach to detect clones in Unified Modeling Language class models. The core of our technique is the construction of a labeled, ranked tree corresponding to the UML class model where attributes with their data types and methods with their signatures are represented as subtrees. By grouping and clustering of repeating subtrees, the tool is able to detect duplications in a UML class model at different levels of granularity i.e. complete class diagram, attributes with their data types and methods with their signatures across the model and cluster of such attributes/methods. We propose a new classification of model clones with the objective of detecting exact and meaningful clones. Empirical evaluation of the tool using open source reverse engineered and forward designed models show some interesting and relevant clones which provide useful insights into software modeling practice.

  17. Detection and Analysis of Clones in UML Class Models

    Dhavleesh Rattan

    2016-01-01

    Full Text Available It is quite frequent to copy and paste code fragments in software development. The copied source code is called a software clone and the activity is referred to as code cloning. The presence of code clones hamper maintenance and may lead to bug propagation. Now-a-days, model driven development has become a standard industry practice. Duplicate parts in models i.e. model clones pose similar challenges as in source code. This paper presents an approach to detect clones in Unified Modeling Language class models. The core of our technique is the construction of a labeled, ranked tree corresponding to the UML class model where attributes with their data types and methods with their signatures are represented as subtrees. By grouping and clustering of repeating subtrees, the tool is able to detect duplications in a UML class model at different levels of granularity i.e. complete class diagram, attributes with their data types and methods with their signatures across the model and cluster of such attributes/methods. We propose a new classification of model clones with the objective of detecting exact and meaningful clones. Empirical evaluation of the tool using open source reverse engineered and forward designed models show some interesting and relevant clones which provide useful insights into software modeling practice.

  18. Focused Crawling of the Deep Web Using Service Class Descriptions

    Rocco, D; Liu, L; Critchlow, T

    2004-06-21

    Dynamic Web data sources--sometimes known collectively as the Deep Web--increase the utility of the Web by providing intuitive access to data repositories anywhere that Web access is available. Deep Web services provide access to real-time information, like entertainment event listings, or present a Web interface to large databases or other data repositories. Recent studies suggest that the size and growth rate of the dynamic Web greatly exceed that of the static Web, yet dynamic content is often ignored by existing search engine indexers owing to the technical challenges that arise when attempting to search the Deep Web. To address these challenges, we present DynaBot, a service-centric crawler for discovering and clustering Deep Web sources offering dynamic content. DynaBot has three unique characteristics. First, DynaBot utilizes a service class model of the Web implemented through the construction of service class descriptions (SCDs). Second, DynaBot employs a modular, self-tuning system architecture for focused crawling of the DeepWeb using service class descriptions. Third, DynaBot incorporates methods and algorithms for efficient probing of the Deep Web and for discovering and clustering Deep Web sources and services through SCD-based service matching analysis. Our experimental results demonstrate the effectiveness of the service class discovery, probing, and matching algorithms and suggest techniques for efficiently managing service discovery in the face of the immense scale of the Deep Web.

  19. An Emerge Approach in Inter Cluster Similarity for Quality Clusters

    H. Venkateswara Reddy

    2013-04-01

    Full Text Available Relationship between the datasets is one most important issue in recent years. The recent methods are based mostly on the numerical data, but these methods are not suitable for real time data such as web pages, business transactions etc., which are known as Categorical data. It is difficult to find relationship in categorical data. In this paper, a new approach is proposed for finding the relationshipbetween the categorical data, hence to find relationship between the clusters. The main aim is to identify the quality clusters based on the relationship between clusters. If there is no relationship between clusters then those clusters are treated as quality clusters.

  20. Vanadogermanate cluster anions.

    Whitfield, T; Wang, X; Jacobson, A J

    2003-06-16

    Three novel vanadogermanate cluster anions have been synthesized by hydrothermal reactions. The cluster anions are derived from the (V(18)O(42)) Keggin cluster shell by substitution of V=O(2+) "caps" by Ge(2)O(OH)(2)(4+) species. In Cs(8)[Ge(4)V(16)O(42)(OH)(4)].4.7H(2)O, 1, (monoclinic, space group C2/c (No. 15), Z = 8, a = 44.513(2) A, b = 12.7632(7) A, c = 22.923(1) A, beta = 101.376(1) degrees ) and (pipH(2))(4)(pipH)(4)[Ge(8)V(14)O(50).(H(2)O)] (pip = C(4)N(2)H(10)), 2 (tetragonal, space group P4(2)/nnm (No. 134), Z = 2, a = 14.9950(7) A, c = 18.408(1) A), two and four VO(2+) caps are replaced, respectively, and each cluster anion encapsulates a water molecule. In K(5)H(8)Ge(8)V(12)SO(52).10H(2)O, 3, (tetragonal, space group I4/m (No. 87), Z = 2, a = 15.573(1) A, c = 10.963(1) A), four VO(2+) caps are replaced by Ge(2)O(OH)(2)(4+) species, and an additional two are omitted. The cluster ion in 3 contains a sulfate anion disordered over two positions. The cluster anions are analogous to the vanadoarsenate anions [V(18)(-)(n)()As(2)(n)()O(42)(X)](m)(-) (X = SO(3), SO(4), Cl; n = 3, 4) previously reported. PMID:12793808

  1. 1919+479: Big WAT in a poor cluster

    Pinkney, Jason; Burns, Jack O.; Hill, John M.

    1994-01-01

    New x-ray, optical, and redshift data are presented for the cluster of galaxies associated with the giant, 1 Mpc diameter, wide-angle tailed (WAT) radio galaxy 1919+479. The ROSAT Position-Sensitive Proportional Counter (PSPC) pointed observation shows an x-ray peak on the WAT and elongated diffuse emission tracing the galaxy distribution. In addition, an asymmetric extension of emission exists between the tails of the WAT. The fitting of a Raymond-Smith thermal model to the x-ray spectra suggests an approximately = 2 keV temperature intracluster medium (ICM). The cooling time and irregular morphology rule out a cluster-wide cooling flow. The x-ray luminosity and temperature are consistent with the velocity dispersion, 480 km/s, estimated from 31 galaxy velocities. However, this velocity distribution is significantly non-Gaussian, which along with the x-ray morphology, suggests incomplete virialization in the cluster. Substructure analysis does not reveal significant clumping in the velocities/positions; but, the spatial distribution of galaxies is very elongated. Also, the cD galaxy producing the WAT does not have a significant radial peculiar velocity with respect to the cluster centroid. These characteristics are consistent with a merger scenario in which a subcluster has crossed the cluster core in the plane of the sky and has dispersed. We compare this cluster with the post-merger cluster Abell 2634 (Pinkney et al., 1993), containing the prototype WAT, and with a recent N-Body/Hydro simulation of merging clusters. The similarities indicate that the cluster 1919+479 may be the poor extreme of a class of clusters in which the bulk motion in the ICM, caused by a subcluster merger, in shaping the central WAT.

  2. Kinematics of Clustering

    Wang, Steven; Metcalfe, Guy; Wu, Jie

    2014-01-01

    The dynamical system for inertial particles in fluid flow has both attracting and repelling regions, the interplay of which can localize particles. In laminar flow experiments we find that particles, initially moving throughout the fluid domain, can undergo an instability and cluster into subdomains of the fluid when the flow Reynolds number exceeds a critical value that depends on particle and fluid inertia. We derive an expression for the instability boundary and for a universal curve that describes the clustering rate for all particles.

  3. South Asian Cluster

    Ionel Sergiu Pirju

    2014-12-01

    Full Text Available This article aims at presenting the South Asian cluster composed of India, Indonesia, Iran and Malaysia, the intercultural values that characterizes it, the supported leadership style and tracing the main macroeconomic considerations which characterizes them. The research is synchronic, analysing the contemporary situation of these countries without reference to their evolution in time, by using the positivist paradigm that explains the reality at one point. It will be analysed the overall cluster with the existing interactions between the countries that composes it, while the article being one of information will avoid building recommendation, or new theories.

  4. Cluster's last stand?

    Lockwood, Mike

    1997-01-01

    On 4 June last year the first attempt to make three-dimensional measurements in space was lost when the Ariane 5 rocket veered off course and self-destructed, 39 s into its maiden flight. On board were four identical spacecraft which made up Cluster,a mission that the European Space Agency called a “cornerstone” of its Horizon 2000 scientific programme. A full description of the Cluster satellites is given in a special issue of Space Science Reviews (Escoubet et al. 1997). Their loss de...

  5. Abelian Non-Global Logarithms from Soft Gluon Clustering

    Kelley, Randall; Zuberi, Saba

    2012-01-01

    Most recombination-style jet algorithms cluster soft gluons in a complex way. This leads to correlations in the soft gluon phase space and introduces logarithmic corrections to jet cross sections. The leading Abelian clustering logarithms occur at least at next-to leading logarithm (NLL) in the exponent of the distribution, and we show that new clustering effects contributing at NLL likely arise at each order. Therefore we find that it is unlikely that clustering logs can be resummed to NLL. Clustering logarithms make the anti-kT algorithm theoretically preferred, for which they are power suppressed. They can arise in Abelian and non-Abelian terms, and we calculate the Abelian clustering logarithms at two loops for the jet mass distribution using the Cambridge/Aachen and kT algorithms, including jet radius dependence, which extends previous results. We find that previously identified logarithms from clustering effects can be naturally thought of as a class of non-global logarithms (NGLs), which have tradition...

  6. The open cluster NGC 7142: interstellar extinction, distance and age

    Straizys, V; Boyle, R P; Zdanavicius, K; Zdanavicius, J; Laugalys, V; Kazlauskas, A

    2013-01-01

    The results of medium-band photometry of 1037 stars in the area of old open cluster NGC 7142 down to V = 20.1 mag in the Vilnius seven-colour system are presented. Photometric results are used to classify in spectral and luminosity classes about 80 percent of stars down to V = 18.5 mag, to identify cluster members, to determine the main cluster parameters and to investigate the interstellar extinction in this direction. The average extinction A_V of the cluster is about 1.1 mag, E(B-V) = 0.35, and its distance is 2.3 kpc (the distance modulus 11.8 mag). The age of the cluster, 3.0 Gyr, is estimated from the intrinsic colour-magnitude diagram with individual dereddening of each star and the Padova isochrones. The surface distribution of the extinction is shown. The reddening of the eclipsing variable V375 Cep is found to be close to the average reddening of the cluster. Probably, the cluster contains five red clump giants, two asymptotic branch stars and four blue stragglers.

  7. The luminosity function of cluster galaxies relations among M$_{1}$, M* and the morphological type

    Trevese, D; Appodia, B

    1996-01-01

    A study of the luminosity function of 36 Abell clusters of galaxies has been carried out using photographic plates obtained with the Palomar 1.2 m Schmidt telescope. The relation between the magnitude M_1 of the brightest cluster member and the Schechter function parameter M* has been analyzed. A positive correlation between M* and M_1 is found. However clusters appear segregated in the M_1-M* plane according to their Rood & Sastry class in such a way that on average M_1 becomes brighter while M* becomes fainter going from late to early Rood & Sastry and also Bautz & Morgan classes. Also a partial correlation analysis involving the magnitude M_10 of the 10th brightest galaxy, shows a negative intrinsic correlation between M_1 and M*. These results agree with the cannibalism model for the formation of brightest cluster members, and provide new constraints for theories of cluster formation and evolution.

  8. Clustering of resting state networks.

    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.

  9. Experiencing a Flipped Mathematics Class

    Larsen, A. Judy

    2013-01-01

    The flipped classroom is an old concept that has recently been redefined through the emergence of new technologies that allow teachers to deliver content out of class time. As such, various approaches for implementation exist. Most prominently, teachers are able to use class time in a student-centered manner, which allows students to experience the classroom in diverse ways. This study focuses on describing these experiences in a particular flipped adult upgrading mathematics class. Students ...

  10. Class Differences in Cohabitation Processes

    Sassler, Sharon; Miller, Amanda J.

    2011-01-01

    Despite the burgeoning cohabitation literature, research has failed to examine social class variation in processes of forming and advancing such unions. Drawing upon in-depth interviews with 122 working- and middle-class cohabitors, we examine the duration between dating and moving in together, reasons for cohabiting, and subsequent plans. Transitions to cohabitation are more rapid among the working class. Respondents often cohabited for practical reasons—out of financial necessity, because i...

  11. Professional Elites in "Classes" Societies

    2003-01-01

    Modern European identity has been forged in class struggles between the French revolution and fall of the Berlin Wall, which fell twice. Once, with the rest of the city in May 1945, when a national socialist alternative to a modernizing mix of parliamentary democracy and market economy crumbled after the hot WWII, and second time in November 1989, when a state socialist alternative crumbled after the Cold War. At the same time working class in the USA abandoned trade unions and class struggle...

  12. Reconciling Virtual Classes with Genericity

    Ernst, Erik

    classes. As a result, a kind of type parameters have been introduced, but they are simple and only used where they excel. Conversely, final definitions of virtual classes have been re- moved from the language, thus making virtual classes more flexible. The result- ing language presents a clearer and more...... flexible trade-off between extensibility and predictability, empowering programmers to choose the right balance....

  13. Compact cluster growth on the half-plane: forest fires in a valley

    Kearney, M J

    2003-01-01

    A two-parameter model on a directed lattice is introduced to represent the growth and spread of clusters on the half-plane. The model exhibits a phase transition in the compact directed percolation universality class between a state where clusters are finite with probability one and a state where clusters are infinite with non-zero probability. In the finite regime, exact expressions are given for the mean perimeter length and area of the generated clusters for a variety of different boundary conditions. An illustrative example is considered, namely a forest fire spreading before a prevailing wind along the floor and sides of an idealized valley.

  14. Compact cluster growth on the half-plane: forest fires in a valley

    A two-parameter model on a directed lattice is introduced to represent the growth and spread of clusters on the half-plane. The model exhibits a phase transition in the compact directed percolation universality class between a state where clusters are finite with probability one and a state where clusters are infinite with non-zero probability. In the finite regime, exact expressions are given for the mean perimeter length and area of the generated clusters for a variety of different boundary conditions. An illustrative example is considered, namely a forest fire spreading before a prevailing wind along the floor and sides of an idealized valley

  15. Combining cluster number counts and galaxy clustering

    Lacasa, Fabien

    2016-01-01

    We present a detailed modelling of the joint covariance matrix between cluster number counts and the galaxy angular power spectrum. To this end, we use a Halo Model framework complemented by a Halo Occupation Distribution model (HOD), and we work in full-sky. We demonstrate the importance of accounting for non-Gaussianity to produce accurate covariance predictions, as the Gaussian part of the covariance can in fact become subdominant in certain configurations. We discuss in particular the case of the super-sample covariance (SSC), including the effects of galaxy shot-noise, halo second order bias and non-local bias, and demonstrating interesting mathematical properties. Using the joint covariance matrix and a Fisher matrix methodology, we examine the prospects of combining these two probes to constrain cosmological and HOD parameters. We find that the combination indeed results in noticeable better constraints, in particular because the cross-covariance introduces a synergy between the probes on small scales....

  16. Data clustering algorithms and applications

    Aggarwal, Charu C

    2013-01-01

    Research on the problem of clustering tends to be fragmented across the pattern recognition, database, data mining, and machine learning communities. Addressing this problem in a unified way, Data Clustering: Algorithms and Applications provides complete coverage of the entire area of clustering, from basic methods to more refined and complex data clustering approaches. It pays special attention to recent issues in graphs, social networks, and other domains.The book focuses on three primary aspects of data clustering: Methods, describing key techniques commonly used for clustering, such as fea

  17. Latent cluster analysis of ALS phenotypes identifies prognostically differing groups.

    Jeban Ganesalingam

    Full Text Available BACKGROUND: Amyotrophic lateral sclerosis (ALS is a degenerative disease predominantly affecting motor neurons and manifesting as several different phenotypes. Whether these phenotypes correspond to different underlying disease processes is unknown. We used latent cluster analysis to identify groupings of clinical variables in an objective and unbiased way to improve phenotyping for clinical and research purposes. METHODS: Latent class cluster analysis was applied to a large database consisting of 1467 records of people with ALS, using discrete variables which can be readily determined at the first clinic appointment. The model was tested for clinical relevance by survival analysis of the phenotypic groupings using the Kaplan-Meier method. RESULTS: The best model generated five distinct phenotypic classes that strongly predicted survival (p<0.0001. Eight variables were used for the latent class analysis, but a good estimate of the classification could be obtained using just two variables: site of first symptoms (bulbar or limb and time from symptom onset to diagnosis (p<0.00001. CONCLUSION: The five phenotypic classes identified using latent cluster analysis can predict prognosis. They could be used to stratify patients recruited into clinical trials and generating more homogeneous disease groups for genetic, proteomic and risk factor research.

  18. Be phenomenon in open clusters: Results from a survey of emission-line stars in young open clusters

    Mathew, Blesson; Bhatt, Bhuwan Chandra

    2008-01-01

    Emission-line stars in young open clusters are identified to study their properties, as a function of age, spectral type and their evolutionary state. 207 open star clusters were observed using slitless spectroscopy method and 157 emission stars were identified in 42 clusters. We have found 54 new emission-line stars in 24 open clusters, out of which 19 clusters are found to house emission stars for the first time. About 20% clusters harbour emission stars. The fraction of clusters housing emission stars is maximum in both the 0--10 and 20--30 Myr age bin ($\\sim$ 40% each) and in the other age bins, this fraction ranges between 10% -- 25%, upto 80 Myr. We have used optical colour magnitude diagram (CMD) along with Near-IR Colour-Colour diagram (NIR CCDm) to classify the emission stars into Classical Be (CBe) stars and Herbig Be (HBe) stars. Most of the emission stars in our survey belong to CBe class ($\\sim$ 92%) while a few are HBe stars ($\\sim$ 6%) and HAe stars ($\\sim$1%). The CBe stars are located all alo...

  19. First class models from linear and nonlinear second class constraints

    Dehghani, Mehdi; Mardaani, Maryam; Monemzadeh, Majid; Nejad, Salman Abarghouei

    2015-10-01

    Two models with linear and nonlinear second class constraints are considered and gauged by embedding in an extended phase space. These models are studied by considering a free non-relativistic particle on the hyperplane and hypersphere in the configuration space. The gauged theory of the first model is obtained by converting the very second class system to the first class one directly. In contrast, the first class system related to the free particle on the hypersphere is derived with the help of the infinite Batalin-Fradkin-Tyutin (BFT) embedding procedure. We propose a practical formula, based on the simplified BFT method, which is practical in gauging linear and some nonlinear second class systems. As a result of gauging these two models, we show that in the conversion of second class constraints to the first class ones, the minimum number of phase space degrees of freedom for both systems is a pair of phase space coordinates. This pair is made up of a coordinate and its conjugate momentum for the first model, but the corresponding Poisson structure of the embedded non-relativistic particle on hypersphere is a nontrivial one. We derive infinite correction terms for the Hamiltonian of the nonlinear constraints and an interacting gauged Hamiltonian is constructed by summing over them. At the end, we find an open algebra for three first class objects of the embedded nonlinear system.

  20. Class Counts: Education, Inequality, and the Shrinking Middle Class

    Ornstein, Allan

    2007-01-01

    Class differences and class warfare have existed since the beginning of western civilization, but the gap in income and wealth between the rich (top 10 percent) and the rest has increased steadily in the last twenty-five years. The U.S. is heading for a financial oligarchy much worse than the aristocratic old world that our Founding Fathers feared…

  1. Identification of nuclear transients via optimized fuzzy clustering

    In this paper, we look into the issue of using cluster analysis for transient classification in nuclear components and systems. In general, the choice of the metrics upon which clustering is based can be critical for obtaining geometric clusters as close as possible to the real physical classes in the feature space. The complexity and variety of cluster shapes and dimensions which can be expected in the transient classification of interest lead us to take an approach based on a different Mahalanobis metric for each cluster. The a priori known information regarding the true classes to which the patterns belong is exploited to select, by means of a supervised evolutionary algorithm, the different optimal Mahalanobis metrics. Further, the diagonal elements of the matrices defining the metrics can be taken as measures of the relevance of the features employed for the classification of the different patterns. The efficiency of the approach is verified with respect to a literature problem and then applied to the case of classification of transients in a nuclear component

  2. A new type of cluster and cluster ion source

    Combining a magnetron gas discharge with the gas aggregation technique an intense source of clusters has been developed. A large part (up to 80%) of the clusters can be generated as ions without using additional electron impact ionisation. (orig.)

  3. Quartile Clustering: A quartile based technique for Generating Meaningful Clusters

    Goswami, Saptarsi

    2012-01-01

    Clustering is one of the main tasks in exploratory data analysis and descriptive statistics where the main objective is partitioning observations in groups. Clustering has a broad range of application in varied domains like climate, business, information retrieval, biology, psychology, to name a few. A variety of methods and algorithms have been developed for clustering tasks in the last few decades. We observe that most of these algorithms define a cluster in terms of value of the attributes, density, distance etc. However these definitions fail to attach a clear meaning/semantics to the generated clusters. We argue that clusters having understandable and distinct semantics defined in terms of quartiles/halves are more appealing to business analysts than the clusters defined by data boundaries or prototypes. On the samepremise, we propose our new algorithm named as quartile clustering technique. Through a series of experiments we establish efficacy of this algorithm. We demonstrate that the quartile clusteri...

  4. Emergence of regional clusters

    Dahl, Michael S.; Østergaard, Christian Richter; Dalum, Bent

    2010-01-01

    The literature on regional clusters has increased considerably during the last decade. The emergence and growth patterns are usually explained by such factors as unique local culture, regional capabilities, tacit knowledge or the existence of location-specific externalities (knowledge spillovers...

  5. Evolution of Galaxy Clustering

    Bagla, J. S.

    1997-01-01

    We show that the galaxy correlation function does not evolve in proportion with the correlation function of the underlying mass distribution. Earliest galaxies cluster very strongly and the amplitude of the galaxy correlation function decreases from this large value. This continues till the average peaks have collapsed, after which, the galaxy correlation function does not evolve very strongly.

  6. Cluster Decline and Resilience

    Østergaard, Christian Richter; Park, Eun Kyung

    -2011. Our longitudinal study reveals that technological lock-in and exit of key firms have contributed to impairment of the cluster’s resilience in adapting to disruptions. Entrepreneurship has a positive effect on cluster resilience, while multinational companies have contradicting effects by bringing in...

  7. Web Data Clustering

    Húsek, Dušan; Pokorný, J.; Řezanková, H.; Snášel, V.

    Vol. 4. Berlin: Springer, 2009 - (Abraham, A.; Hassanien, A.; de Carvalho, A.), s. 325-353. (Studies in Computational Intelligence . 204). ISBN 978-3-642-01087-3 R&D Projects: GA AV ČR 1ET100300419 Institutional research plan: CEZ:AV0Z10300504 Keywords : clustering methods * web environment * neural networks Subject RIV: BB - Applied Statistics, Operational Research

  8. Curriculum Guide Construction Cluster.

    Kline, Ken

    As part of a model construction cluster curriculum development project, this guide was developed and implemented in the Beaverton (Oregon) School District. The curriculum guide contains 16 units covering the following topics: introduction to construction jobs; safety and first aid; blueprint readings; basic mathematics; site work; framing; roofing…

  9. Galactic Open Clusters

    Von Hippel, T

    2005-01-01

    The study of open clusters has a classic feel to it since the subject predates anyone alive today. Despite the age of this topic, I show via an ADS search that its relevance and importance in astronomy has grown faster in the last few decades than astronomy in general. This is surely due to both technical reasons and the interconnection of the field of stellar evolution to many branches of astronomy. In this review, I outline what we know today about open clusters and what they have taught us about a range of topics from stellar evolution to Galactic structure to stellar disk dissipation timescales. I argue that the most important astrophysics we have learned from open clusters is stellar evolution and that its most important product has been reasonably precise stellar ages. I discuss where open cluster research is likely to go in the next few years, as well as in the era of 20m telescopes, SIM, and GAIA. Age will continue to be of wide relevance in astronomy, from cosmology to planet formation timescales, an...

  10. Fuzzy clustering of mechanisms

    Amitabha Ghosh; Dilip Kumar Pratihar; M V V Amarnath; Guenter Dittrich; Jorg Mueller

    2012-10-01

    During the course of development of Mechanical Engineering, a large number of mechanisms (that is, linkages to perform various types of tasks) have been conceived and developed. Quite a few atlases and catalogues were prepared by the designers of machines and mechanical systems. However, often it is felt that a clustering technique for handling the list of large number of mechanisms can be very useful,if it is developed based on a scientific principle. In this paper, it has been shown that the concept of fuzzy sets can be conveniently used for this purpose, if an adequate number of properly chosen attributes (also called characteristics) are identified. Using two clustering techniques, the mechanisms have been classified in the present work and in future, it may be extended to develop an expert system, which can automate type synthesis phase of mechanical design. To the best of the authors’ knowledge, this type of clustering of mechanisms has not been attempted before. Thus, this is the first attempt to cluster the mechanisms based on some quantitative measures. It may help the engineers to carry out type synthesis of the mechanisms.

  11. The Cluster Wind from Local Massive Star Clusters

    Stevens, Ian R.; Hartwell, Joanna M.

    2003-01-01

    Results of a study of the theoretically predicted and observed X-ray properties of local massive star clusters are presented, with a focus on understanding the mass and energy flow from these clusters into the ISM via a cluster wind. A simple theoretical model, based on the work of Chevalier & Clegg (1985), is used to predict the theoretical cluster properties, and these are compared to those obtained from recent Chandra observations. The model includes the effect of lower energy transfer eff...

  12. Quartile Clustering: A quartile based technique for Generating Meaningful Clusters

    Goswami, Saptarsi; Chakrabarti, Amlan

    2012-01-01

    Clustering is one of the main tasks in exploratory data analysis and descriptive statistics where the main objective is partitioning observations in groups. Clustering has a broad range of application in varied domains like climate, business, information retrieval, biology, psychology, to name a few. A variety of methods and algorithms have been developed for clustering tasks in the last few decades. We observe that most of these algorithms define a cluster in terms of value of the attributes...

  13. Globular clusters in the Hydra I cluster of galaxies

    Excess faint stellar images with B magnitudes about 24 are found surrounding the elliptical galaxt NGC 3311 in the Hydra I cluster (V=3450 km S-1). The magnitudes and number of these images agree well with those expected if (1) NGC 3311 is surrounded by a system of globular clusters identical to that surrounding M87 in the Virgo cluster and (2) the distances to the Hydra I and Virgo clusters are proportional to their velocities

  14. The Rotation of Galaxy Clusters

    Tovmassian, H. M.

    2015-09-01

    The method for detection of the galaxy cluster rotation based on the study of distribution of member galaxies with velocities lower and higher than the cluster mean velocity over the cluster image is proposed. The search for rotation is made for flat clusters with a/b > 1.8 and BMI type clusters which are expected to be rotating. For comparison there were studied also round clusters and clusters of NBMI type, the second by brightness galaxy, which does not differ significantly from the cluster cD galaxy. Seventeen out of studied 65 clusters are found to be rotating. It was found that the detection rate is sufficiently high for flat clusters, over 60%, and clusters of BMI type with dominant cD galaxy, ≈ 35% . The obtained results show that clusters were formed from the huge primordial gas clouds and preserved the rotation of the primordial clouds, unless they did not experience mergings with other clusters and groups of galaxies, as a result of which the rotation was prevented.

  15. The rotation of Galaxy Clusters

    Tovmassian, Hrant M

    2015-01-01

    The method for detection of the galaxy cluster rotation based on the study of distribution of member galaxies with velocities lower and higher of the cluster mean velocity over the cluster image is proposed. The search for rotation is made for flat clusters with $a/b>1.8$ and BMI type clusters which are expected to be rotating. For comparison there were studied also round clusters and clusters of NBMI type, the second by brightness galaxy in which does not differ significantly from the cluster cD galaxy. Seventeen out of studied 65 clusters are found to be rotating. It was found that the detection rate is sufficiently high for flat clusters, over 60\\%, and clusters of BMI type with dominant cD galaxy, ~ 35%. The obtained results show that clusters were formed from the huge primordial gas clouds and preserved the rotation of the primordial clouds, unless they did not have merging with other clusters and groups of galaxies, in the result of which the rotation has been prevented.

  16. Choosing the Number of Clusters in K-Means Clustering

    Steinley, Douglas; Brusco, Michael J.

    2011-01-01

    Steinley (2007) provided a lower bound for the sum-of-squares error criterion function used in K-means clustering. In this article, on the basis of the lower bound, the authors propose a method to distinguish between 1 cluster (i.e., a single distribution) versus more than 1 cluster. Additionally, conditional on indicating there are multiple…

  17. Single-cluster dynamics for the random-cluster model

    Deng, Y.; Qian, X.; Blöte, H.W.J.

    2009-01-01

    We formulate a single-cluster Monte Carlo algorithm for the simulation of the random-cluster model. This algorithm is a generalization of the Wolff single-cluster method for the q-state Potts model to noninteger values q>1. Its results for static quantities are in a satisfactory agreement with those

  18. Applying Data Clustering Feature to Speed Up Ant Colony Optimization

    Chao-Yang Pang; Ben-Qiong Hu; Jie Zhang; Wei Hu; Zheng-Chao Shan

    2013-01-01

    Ant colony optimization (ACO) is often used to solve optimization problems, such as traveling salesman problem (TSP). When it is applied to TSP, its runtime is proportional to the squared size of problem $N$ so as to look less efficient. The following statistical feature is observed during the authors’ long-term gene data analysis using ACO: when the data size $N$ becomes big, local clustering appears frequently. That is, some data cluster tightly in a small area and form a class, and the cor...

  19. Segmentation of Nonstationary Time Series with Geometric Clustering

    Bocharov, Alexei; Thiesson, Bo

    2013-01-01

    We introduce a non-parametric method for segmentation in regimeswitching time-series models. The approach is based on spectral clustering of target-regressor tuples and derives a switching regression tree, where regime switches are modeled by oblique splits. Such models can be learned efficiently...... from data, where clustering is used to propose one single split candidate at each split level. We use the class of ART time series models to serve as illustration, but because of the non-parametric nature of our segmentation approach, it readily generalizes to a wide range of time-series models that go...

  20. Monopole clusters in Abelian projected gauge theories

    Hart, A

    1998-01-01

    We show that the monopole currents which one obtains in the maximally Abelian gauge of SU(2) fall into two quite distinct classes (when the volume is large enough). In each field configuration there is precisely one cluster that permeates the whole lattice volume. It has a current density and a magnetic screening mass that scale and it produces the whole of the string tension. The remaining clusters have a number density that follows an approximate power law proportional to the inverse cube of l where l is the length of the monopole world line in lattice units. These clusters are localised in space-time with radii which vary as the square root of l. In terms of the radius r these `lumps' have a scale-invariant distribution proportional to (dr/r . 1/{r^4}). Moreover they appear not to contribute at all to the string tension. The fact that they are scale-invariant at small distances would seem to rule out an instanton origin.

  1. Eclipsing binaries in open clusters

    Southworth, John; Clausen, J.V.

    Stars: fundamental parameters - Stars : binaries : eclipsing - Stars: Binaries: spectroscopic - Open clusters and ass. : general Udgivelsesdato: 5 August......Stars: fundamental parameters - Stars : binaries : eclipsing - Stars: Binaries: spectroscopic - Open clusters and ass. : general Udgivelsesdato: 5 August...

  2. Cluster algebras and derived categories

    Keller, Bernhard

    2012-01-01

    This is an introductory survey on cluster algebras and their (additive) categorification using derived categories of Ginzburg algebras. After a gentle introduction to cluster combinatorics, we review important examples of coordinate rings admitting a cluster algebra structure. We then present the general definition of a cluster algebra and describe the interplay between cluster variables, coefficients, c-vectors and g-vectors. We show how c-vectors appear in the study of quantum cluster algebras and their links to the quantum dilogarithm. We then present the framework of additive categorification of cluster algebras based on the notion of quiver with potential and on the derived category of the associated Ginzburg algebra. We show how the combinatorics introduced previously lift to the categorical level and how this leads to proofs, for cluster algebras associated with quivers, of some of Fomin-Zelevinsky's fundamental conjectures.

  3. The Evolution of Cluster Substructure

    Jeltema, T E; Bautz, M W; Buote, D A; Jeltema, Tesla E.; Canizares, Claude R.; Bautz, Mark W.; Buote, David A.

    2003-01-01

    Using Chandra archival data, we have begun a study to quantify the evolution of cluster morphology with redshift. To quantify cluster morphology, we use the power ratio method developed by Buote and Tsai (1995). Power ratios are constructed from moments of the two-dimensional gravitational potential and are, therefore, related to a cluster's dynamical state. Our sample will include around 50 clusters from the Chandra archive with redshifts between 0.11 and 1.1. These clusters were selected from two fairly complete flux-limited X-ray surveys (the ROSAT Bright Cluster Sample and the Einstein Medium Sensitivity Survey), and additional high-redshift clusters were selected from recent ROSAT flux-limited surveys. Here we present preliminary results from the first 15 clusters in this sample. Of these, eight have redshifts below 0.5, and seven have redshifts above 0.5.

  4. Structure and bonding in clusters

    We review here the recent progress made in the understanding of the electronic and atomic structure of small clusters of s-p bonded materials using the density functional molecular dynamics technique within the local density approximation. Starting with a brief description of the method, results are presented for alkali metal clusters, clusters of divalent metals such as Mg and Be which show a transition from van der Waals or weak chemical bonding to metallic behaviour as the cluster size grows and clusters of Al, Sn and Sb. In the case of semiconductors, we discuss results for Si, Ge and GaAs clusters. Clusters of other materials such as P, C, S, and Se are also briefly discussed. From these and other available results we suggest the possibility of unique structures for the magic clusters. (author). 69 refs, 7 figs, 1 tab

  5. The Paradox of Paperless Classes.

    Lackie, Paula

    1998-01-01

    Describes paperless classes developed at Carleton College that augment traditional classes by giving students and faculty the ability to share digital course-related materials via the campus computer network. Presents a case study of a managerial economics course, and includes problems with various aspects of the course and solutions. (LRW)

  6. Class, Identity, and Teacher Education

    Van Galen, Jane A.

    2010-01-01

    This paper explores the possibilities of working with White, working-class teacher education students to explore the "complex social trajectory" (Reay in Women's Stud Int Forum 20(2):225-233, 1997a, p. 19) of class border crossing as they progress through college. Through analysis of a course that I have developed, "Education and the American…

  7. Social Class and the Extracurriculum

    Barratt, Will

    2012-01-01

    Social class is a powerful and often unrecognized influence on student participation in the extracurriculum. Spontaneous student-created extracurricular experiences depend on students affiliating and interacting with each other; student social class is a powerful influence on student affiliations. Students tend to exercise consciousness of kind-…

  8. A Touch of...Class!

    Netten, Joan W., Ed.

    1984-01-01

    A collection of ideas for class activities in elementary and secondary language classes includes a vocabulary review exercise and games of memory, counting, vocabulary, flashcard tic-tac-toe, dice, trashcans, questioning, and spelling. Some are designed specifically for French. (MSE)

  9. Class Differences in Cohabitation Processes

    Sassler, Sharon; Miller, Amanda J.

    2011-01-01

    Despite the burgeoning cohabitation literature, research has failed to examine social class variation in processes of forming and advancing such unions. Drawing upon in-depth interviews with 122 working- and middle-class cohabitors, we examine the duration between dating and moving in together, reasons for cohabiting, and subsequent plans.…

  10. The Generalizability of Class Means

    Kane, Michael T.; Brennan, Robert L.

    1977-01-01

    Dependability of class means is analyzed by applying generalizability to a split-plot design with students nested within classes. Basic generalizability concepts are reviewed, and the derivation and interpretation of distinct generalizability concepts are discussed. Four generalizability coefficients are compared with each other and with the three…

  11. Student Engagement and Marketing Classes

    Taylor, Steven A.; Hunter, Gary L.; Melton, Horace; Goodwin, Stephen A.

    2011-01-01

    A study is reported that investigates the goals underlying undergraduate students' engagement in their major classes, nonmajor classes, and in extracurricular activities. The qualitative study employs both focus groups and goal-mapping exercises. The results suggest that students tend to focus on utilitarian, attribute-level considerations mainly…

  12. Predicting Acoustics in Class Rooms

    Christensen, Claus Lynge; Rindel, Jens Holger

    2005-01-01

    Typical class rooms have fairly simple geometries, even so room acoustics in this type of room is difficult to predict using today's room acoustic computer modeling software. The reasons why acoustics of class rooms are harder to predict than acoustics of complicated concert halls might be...

  13. Dynamic class methods in Java

    Heinlein, Christian

    2005-01-01

    The concept of dynamic class methods in Java, constituting a specialization of a general new programming language concept called dynamic routines, is introduced and applied to a simple case study. Its advantages over standard object-oriented programming techniques including design patterns are demonstrated. Furthermore, an implementation of dynamic class methods as a precompiler-based language extension to Java is described.

  14. The Rise of Skills: Human Capital, the Creative Class and Regional Development

    Mellander, Charlotta; Florida, Richard

    2012-01-01

    The past couple of decades have seen what amounts to skills revolution in urban and regional economic research. From industrial location theory and Alfred Marshall’s concern for agglomeration to more recent research on high-tech districts and industrial clusters firms and industries has been the dominant unit of analysis. But since the 1990s there has been a growing focus on skills. This broad research thrust includes studies of human capital; the creative class and occupational class more br...

  15. Control Class Summaries and Control Class IV from April 1990

    Wu, J.; /Fermilab

    1991-02-22

    The D0 cryogenic control system is a complicated system with many facets. Because of the large number and variety of features in the system, a series of ongoing control system training seminars, or control classes, were created in order to keep people up to date on the operation of the system. As of the writing of this engineering note, there have been four classes. The original lecture notes from each class can be found in the cryogenic control room at the D0 Assembly Building, or in the Co-op office. This note provides a summary of the first three control classes, and it includes the entire set of notes from the fourth class, which was held in April of 1990. This class was taught by Jeff Wendlandt and Dan Markley. Dan should be consulted for more complete explanations than those given in the notes. The notes are, in fact, more of a reference for someone who has some experience with the system, than they are a training manual. Most of the pages include pictures and printouts of different menus and functions, useful for finding details without searching through the actual program. In general, this note serves as a pointer to the existence of the control class lecture notes, and as an explanation of their overall contents and purpose.

  16. First class models from linear and nonlinear second class constraints

    Dehghani, Mehdi; Monemzadeh, Majid; Abarghooeinejad, Salman

    2014-01-01

    Two models with linear and nonlinear second class constraints are considered and gauged by embedding in an extended phase space. These models are the free non-relativistic particle on a hyperplane and hyper sphere in configuration space. For the first model we construct its gauged corresponding by the condition of converting second class system to first class one, directly. In contrast the first class system related to the free particle on hyper sphere is derived by the BFT embedding procedure, where its steps are infinite. We give a practical formula for gauging linear and some of the nonlinear second class systems, based on the simplified BFT method. As a result of the gauging two models, we show that in the conversion of second class to the first class constraints the minimum number of phase space degrees of freedom for both systems is a pair of phase space coordinate. This pair for first system is a coordinate and its momentum conjugate, but Poisson structure of embedded non-relativistic particle on hyper...

  17. Resampling methods for document clustering

    Volk, D.; Stepanov, M. G.

    2001-01-01

    We compare the performance of different clustering algorithms applied to the task of unsupervised text categorization. We consider agglomerative clustering algorithms, principal direction divisive partitioning and (for the first time) superparamagnetic clustering with several distance measures. The algorithms have been applied to test databases extracted from the Reuters-21578 text categorization test database. We find that simple application of the different clustering algorithms yields clus...

  18. Privacy Preserving Distributed DBSCAN Clustering

    Jinfei Liu; Li Xiong; Jun Luo; Joshua Zhexue Huang

    2013-01-01

    DBSCAN is a well-known density-based clustering algorithm which offers advantages for finding clusters of arbitrary shapes compared to partitioning and hierarchical clustering methods. However, there are few papers studying the DBSCAN algorithm under the privacy preserving distributed data mining model, in which the data is distributed between two or more parties, and the parties cooperate to obtain the clustering results without revealing the data at the individual parties. In this paper, we...

  19. A Tutorial on Spectral Clustering

    Von Luxburg, Ulrike

    2007-01-01

    In recent years, spectral clustering has become one of the most popular modern clustering algorithms. It is simple to implement, can be solved efficiently by standard linear algebra software, and very often outperforms traditional clustering algorithms such as the k-means algorithm. On the first glance spectral clustering appears slightly mysterious, and it is not obvious to see why it works at all and what it really does. The goal of this tutorial is to give some intuition on those questions...

  20. Experiments on Graph Clustering Algorithms

    Brandes, Ulrik; Gaertler, Marco; Wagner, Dorothea

    2003-01-01

    A promising approach to graph clustering is based on the intuitive notion of intra-cluster density vs. inter-cluster sparsity. While both formalizations and algorithms focusing on particular aspects of this rather vague concept have been proposed no conclusive argument on their appropriateness has been given. As a first step towards understanding the consequences of particular conceptions, we conducted an experimental evaluation of graph clustering approaches. By combining proven techniques f...

  1. Practical Introduction to Clustering Data

    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.

  2. [Cluster analysis in biomedical researches].

    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. PMID:24640781

  3. Alpha clustering in 18F

    We review some of the key experimental and theoretical studies of α-clustering in 18F. Particular attention is given to the 4p-2h nature of such α-clustered states, and the interaction between the holes and clusters is examined in terms of both weak and strongcoupling regimes. The experimental work focuses on α-transfer spectroscopy and α resonant scattering as tools for investigating α-clustering

  4. Textile Industrial Clusters in China

    Nie Ting

    2010-01-01

    @@ "National Textile Industry Cluster Development Seminar" convened,held by China National Textile and Apparel Council,23 cities and towns were awarded as China's Textile Industry Cluster Pilot District.By far,China's textile industrial clusters have grown to 164,which indicate a quick and gorgeous development.These textile industrial clusters have a great impact on the local economy and even the whole national textile industry's development.

  5. Clustering analysis using Swarm Intelligence

    Farmani, Mohammad Reza

    2016-01-01

    This thesis is concerned with the application of the swarm intelligence methods in clustering analysis of datasets. The main objectives of the thesis are ∙ Take the advantage of a novel evolutionary algorithm, called artificial bee colony, to improve the capability of K-means in finding global optimum clusters in nonlinear partitional clustering problems. ∙ Consider partitional clustering as an optimization problem and an improved antbased algorithm, named Opposition-Based A...

  6. Categorization Axioms for Clustering Results

    Yu, Jian; Xu, Zongben

    2014-01-01

    Cluster analysis has attracted more and more attention in the field of machine learning and data mining. Numerous clustering algorithms have been proposed and are being developed due to diverse theories and various requirements of emerging applications. Therefore, it is very worth establishing an unified axiomatic framework for data clustering. In the literature, it is an open problem and has been proved very challenging. In this paper, clustering results are axiomatized by assuming that an p...

  7. Defining Clusters of Related Industries

    Mercedes Delgado; Porter, Michael E.; Scott Stern

    2014-01-01

    Clusters are geographic concentrations of industries related by knowledge, skills, inputs, demand, and/or other linkages. A growing body of empirical literature has shown the positive impact of clusters on regional and industry performance, including job creation, patenting, and new business formation. There is an increasing need for cluster-based data to support research, facilitate comparisons of clusters across regions, and support policymakers and practitioners in defining regional strate...

  8. Predictive Overlapping Co-Clustering

    Sarkar, Chandrima; Srivastava, Jaideep

    2014-01-01

    In the past few years co-clustering has emerged as an important data mining tool for two way data analysis. Co-clustering is more advantageous over traditional one dimensional clustering in many ways such as, ability to find highly correlated sub-groups of rows and columns. However, one of the overlooked benefits of co-clustering is that, it can be used to extract meaningful knowledge for various other knowledge extraction purposes. For example, building predictive models with high dimensiona...

  9. A CLUE for CLUster Ensembles

    Kurt Hornik

    2005-01-01

    Cluster ensembles are collections of individual solutions to a given clustering problem which are useful or necessary to consider in a wide range of applications. The R package clue provides an extensible computational environment for creating and analyzing cluster ensembles, with basic data structures for representing partitions and hierarchies, and facilities for computing on these, including methods for measuring proximity and obtaining consensus and "secondary" clusterings....

  10. The Assembly of Galaxy Clusters

    Berrier, Joel C.; Stewart, Kyle R.; Bullock, James S.; Purcell, Chris W.; Barton, Elizabeth J.; Wechsler, Risa H.

    2008-05-16

    We study the formation of fifty-three galaxy cluster-size dark matter halos (M = 10{sup 14.0-14.76} M{sub {circle_dot}}) formed within a pair of cosmological {Lambda}CDM N-body simulations, and track the accretion histories of cluster subhalos with masses large enough to host {approx} 0.1L{sub *} galaxies. By associating subhalos with cluster galaxies, we find the majority of galaxies in clusters experience no 'pre-processing' in the group environment prior to their accretion into the cluster. On average, {approx} 70% of cluster galaxies fall into the cluster potential directly from the field, with no luminous companions in their host halos at the time of accretion; and less than {approx} 12% are accreted as members of groups with five or more galaxies. Moreover, we find that cluster galaxies are significantly less likely to have experienced a merger in the recent past ({approx}< 6 Gyr) than a field halo of the same mass. These results suggest that local, cluster processes like ram-pressure stripping, galaxy harassment, or strangulation play the dominant role in explaining the difference between cluster and field populations at a fixed stellar mass; and that pre-evolution or past merging in the group environment is of secondary importance for setting cluster galaxy properties for most clusters. The accretion times for z = 0 cluster members are quite extended, with {approx} 20% incorporated into the cluster halo more than 7 Gyr ago and {approx} 20% within the last 2 Gyr. By comparing the observed morphological fractions in cluster and field populations, we estimate an approximate time-scale for late-type to early-type transformation within the cluster environment to be {approx} 6 Gyr.

  11. Familial aggregation of cluster headache

    Cruz, S; Lemos, C; Monteiro, JM

    2013-01-01

    Several studies suggest a strong familial aggregation for cluster headache (CH), but so far none of them have included subjects with probable cluster headache (PCH) in accordance with the International Classification of Headache Disorders. OBJECTIVE: To identify cases of probable cluster headache and to assess the familial aggregation of cluster headache by including these subjects. METHOD: Thirty-six patients attending a headache consultation and diagnosed with trigeminal autonom...

  12. Familial aggregation of cluster headache

    Simao Cruz; Carolina Lemos; Jose Maria Pereira Monteiro

    2013-01-01

    Several studies suggest a strong familial aggregation for cluster headache (CH), but so far none of them have included subjects with probable cluster headache (PCH) in accordance with the International Classification of Headache Disorders. Objective To identify cases of probable cluster headache and to assess the familial aggregation of cluster headache by including these subjects. Method Thirty-six patients attending a headache consultation and diagnosed with trigeminal autonomic headache...

  13. On clusters and clustering from atoms to fractals

    Reynolds, PJ

    1993-01-01

    This book attempts to answer why there is so much interest in clusters. Clusters occur on all length scales, and as a result occur in a variety of fields. Clusters are interesting scientifically, but they also have important consequences technologically. The division of the book into three parts roughly separates the field into small, intermediate, and large-scale clusters. Small clusters are the regime of atomic and molecular physics and chemistry. The intermediate regime is the transitional regime, with its characteristics including the onset of bulk-like behavior, growth and aggregation, a

  14. Cluster automorphism groups of cluster algebras of finite type

    Chang, Wen; Zhu, Bin

    2015-01-01

    We study the cluster automorphism group $Aut(\\mathcal{A})$ of a coefficient free cluster algebra $\\mathcal{A}$ of finite type. A cluster automorphism of $\\mathcal{A}$ is a permutation of the cluster variable set $\\mathscr{X}$ that is compatible with cluster mutations. We show that, on the one hand, by the well-known correspondence between $\\mathscr{X}$ and the almost positive root system $\\Phi_{\\geq -1}$ of the corresponding Dynkin type, the piecewise-linear transformations $\\tau_+$ and $\\tau...

  15. Analytical Approximations to Galaxy Clustering

    Mo, H. J.

    1997-01-01

    We discuss some recent progress in constructing analytic approximations to the galaxy clustering. We show that successful models can be constructed for the clustering of both dark matter and dark matter haloes. Our understanding of galaxy clustering and galaxy biasing can be greatly enhanced by these models.

  16. Adaptive Clustering of Hypermedia Documents.

    Johnson, Andrew; Fotouhi, Farshad

    1996-01-01

    Discussion of hypermedia systems focuses on a comparison of two types of adaptive algorithm (genetic algorithm and neural network) in clustering hypermedia documents. These clusters allow the user to index into the nodes to find needed information more quickly, since clustering is "personalized" based on the user's paths rather than representing…

  17. The Globular Cluster Luminosity Function

    McLaughlin, Dean E.

    2003-01-01

    The main aspects of the globular cluster luminosity function needing to be explained by a general theory of cluster formation are reviewed, and the importance of simultaneously understanding globular cluster systematics (the fundamental plane) within such a theory is pointed out.

  18. Programming with MPI on clusters

    The authors discuss the current state of development for the key aspects of MPI programming on clusters. These aspects are the evolution of the MPI Standard itself, developments in cluster hardware and system software that directly affect MPI implementations, and supporting software that facilitates the use of MPI on scalable clusters. In each case we give a brief background and summarize the current status

  19. The Development of Nanotechnological Clusters as the Elements of Nanoindustrial Infrastructure: European Experience

    Beloglazova Svetlana Anatolyevna

    2015-05-01

    Full Text Available The international experience in managing innovative development of regions shows that at this level of global economic system there is an objective process of synthesis of scientific, industrial, economic and social policies in the form of peculiar entities, named clusters, which helps to create favorable environment for appearance and expansion of innovations. Development strategies of advanced countries embody the identification of key competencies of regions and creation of nanotechnological clusters in order to stimulate innovation. Such clusters are intended to develop the methods for nanotechnology application, create new types of business activities, and provide world-class quality. The importance of implementation of nanotechnological cluster policy being a factor of sustainable development of economic systems at micro-, meso- and macro- levels determines the necessity to consider the experience of successful nanotechnological clusters in Italy and France: the cluster of nanotechnologies in the Veneto region, which is the largest and most competitive cluster in Italy, generating up to 9.3 % of the Italy’s GDP, and the Minalogic cluster in the French region Grenoble, being included in the top 5 largest micro and nanotechnological clusters of the world while Grenoble is in the top 15 most innovative regions in the world. The author defines the largest cluster members, describes key areas and key measures of government and non-government support, analyzes economic performance of clusters and describes their impact on the economy of a region and a country as a whole.

  20. A Simple Alternative to Jet-Clustering Algorithms

    Georgi, Howard

    2014-01-01

    I describe a class of iterative jet algorithms that are based on maximizing a fixed function of the total 4-momentum rather than clustering of pairs of jets. I describe some of the properties of the simplest examples of this class, appropriate for jets at an $e^+e^-$ machine. These examples are sufficiently simple that many features of the jets that they define can be determined analytically with ease. The jets constructed in this way have some potentially useful properties, including a strong form of infrared safety.

  1. Support Policies in Clusters: Prioritization of Support Needs by Cluster Members According to Cluster Life Cycle

    Gulcin Salıngan

    2012-07-01

    Full Text Available Economic development has always been a moving target. Both the national and local governments have been facing the challenge of implementing the effective and efficient economic policy and program in order to best utilize their limited resources. One of the recent approaches in this area is called cluster-based economic analysis and strategy development. This study reviews key literature and some of the cluster based economic policies adopted by different governments. Based on this review, it proposes “the cluster life cycle” as a determining factor to identify the support requirements of clusters. A survey, designed based on literature review of International Cluster support programs, was conducted with 30 participants from 3 clusters with different maturity stage. This paper discusses the results of this study conducted among the cluster members in Eskişehir- Bilecik-Kütahya Region in Turkey on the requirement of the support to foster the development of related clusters.

  2. Di - lambpha cluster states

    The lightest (p, n, Λ) closed-shell hypernucleus sub(ΛΛ) sup(6)He can be considered as a most likely candidate for the unit of hypernuclear cluster structure. First the internal structure of lambpha, α sub(Λ) = sub(ΛΛ) sup(6)He, is investigated by solving the α + Λ + Λ three-body problem microscopically. The compact h. o. wave function (0 s)6 is found to be a good description for α sub(Λ). Secondly, by using the fully microscopic GCM, we have demonstrated that di - α sub(Λ) cluster states constitute a characteristic rotational band of J = 0+ -- 6+. The E2 transition rate from particle - stable 2+ to 0+ states is predicted to be 2 - order faster than the weak decay rate of this system. (author)

  3. Clustering Techniques in Bioinformatics

    Muhammad Ali Masood; M.N.A. Khan

    2015-01-01

    Dealing with data means to group information into a set of categories either in order to learn new artifacts or understand new domains. For this purpose researchers have always looked for the hidden patterns in data that can be defined and compared with other known notions based on the similarity or dissimilarity of their attributes according to well-defined rules. Data mining, having the tools of data classification and data clustering, is one of the most powerful techniques to deal with dat...

  4. Clusters and entrepreneurship

    Delgado, Mercedes; Porter, Michael E.; Stern, Scott, 1969-

    2010-01-01

    This article examines the role of regional clusters in regional entrepreneurship. We focus on the distinct influences of convergence and agglomeration on growth in the number of start-up firms as well as in employment in these new firms in a given region-industry. While reversion to the mean and diminishing returns to entrepreneurship at the region-industry level can result in a convergence effect, the presence of complementary economic activity creates externalities that enhance incentives a...

  5. Swelling of percolation clusters

    Schulz, Michael

    1992-01-01

    The swelling of percolation clusters as models for gelling branched polymers is analyzed by using a simple mean-field approach (for all dimensions) and a Monte-Carlo simulation (for d=3, bond fluctuation method). The numerical swelling exponent μ'=0.443 ± 0.008 shows a significant deviation from the lattice animals solution μ'=0.5, which is caused by the difference between quenched and annealed average procedures.

  6. Multicanonical Cluster Algorithm

    Rummukainen, K.

    1992-01-01

    In this talk I present a multicanonical hybrid-like two-step algorithm, which consists of a microcanonical spin system update with demons, and a multicanonical demon refresh. The demons act as a buffer between the multicanonical heat bath and the spin system, allowing for a large variety of update schemes. In this work the cluster algorithm is demonstrated with the 2-dimensional 7-state Potts model, using volumes up to $128^2$.

  7. Cities as Spatial Clusters

    Ferdinand Rauch

    2013-01-01

    This paper shows that Zipf's Law for cities can emerge as a property of a clustering process. If initially uniformly distributed people chose their location based on a specific gravity equation as found in trade studies, they will form cities that follow Zipf's Law in expected value. This view of cities as spatial agglomerations is supported empirically by the observation that larger cities are surrounded by larger hinterland areas and larger countryside populations.

  8. Clusters, Governance and Sustainability

    Neto, Paulo; Serrano, Maria Manuel

    2010-01-01

    One of the most important aspects of current economical and social reality of each local and regional territory, and very much determinative for its economic development potential, is the nature of its territorial organization of the productive processes, as well as, the characteristics, and sophistication level, of the enterprise strategies that are functioning in it. This paper seeks to contribute to the ongoing discussion on the role of clusters as engines of economic and social developmen...

  9. Cosmology, Clusters and Calorimeters

    Figueroa-Feliciano, Enectali

    2005-01-01

    I will review the current state of Cosmology with Clusters and discuss the application of microcalorimeter arrays to this field. With the launch of Astro-E2 this summer and a slew of new missions being developed, microcalorimeters are the next big thing in x-ray astronomy. I will cover the basics and not-so-basic concepts of microcalorimeter designs and look at the future to see where this technology will go.

  10. Clustering of population pyramids

    Kejžar, Nataša; Korenjak-Černe, Simona; Batagelj, Vladimir

    2015-01-01

    Population pyramid is a very popular presentation of the age-sex distribution of the human population of a particular region. The shape of the pyramid shows many demographic, social, and political characteristics of the time and the region. In the paper results of hierarchical clustering of the world countries based on population pyramids are presented. Special attention is given to the shapes of the pyramids. The changes of the pyramids' shapes, and also changes of the countries inside main ...

  11. Clustering of population pyramids:

    Batagelj, Vladimir; Kejžar, Nataša; Korenjak-Černe, Simona

    2008-01-01

    Population pyramid is a very popular presentation of the age-sex distribution of the human population of a particular region. The shape of the pyramid shows many demographic, social, and political characteristics of the time and the region. In the paper results of hierarchical clustering of the world countries based on population pyramids are presented. Special attention is given to the shapes of the pyramids. The changes of the pyramids' shapes, and also changes of the countries inside main ...

  12. Clustering using Genetic Algorithms

    Kudová, Petra

    Ostrava : VŠB Technická univerzita, 2007 - (Snášel, V.; Platoš, J.), s. 1-11 ISBN 978-80-248-1332-5. [WETDAP 2007. Workshop in Conjunction with Znalosti 2007 /1./. Ostrava (CZ), 22.02.2007-22.02.2007] R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : evolutionary techniques * genetic algorithms * unsupervised learning * clustering Subject RIV: IN - Informatics, Computer Science

  13. Clustering Genetic Algorithm

    Kudová, Petra

    Los Alamitos : IEEE, 2007 - (Tjoa, A.; Wagner, R.), s. 138-142 ISBN 978-0-7695-2932-5. [ETID '07. International Workshop on Evolutionary Techniques /1./, DEXA 2007 International Conference /18./. Regensburg (DE), 03.09.2007-07.09.2007] R&D Projects: GA AV ČR 1ET100300414 Institutional research plan: CEZ:AV0Z10300504 Keywords : clustering * genetic algorithms * k-means Subject RIV: IN - Informatics, Computer Science

  14. Dynamic Clustering of Histogram Data Based on Adaptive Squared Wasserstein Distances

    Irpino, Antonio; De Carvalho, Francisco de AT

    2011-01-01

    This paper deals with clustering methods based on adaptive distances for histogram data using a dynamic clustering algorithm. Histogram data describes individuals in terms of empirical distributions. These kind of data can be considered as complex descriptions of phenomena observed on complex objects: images, groups of individuals, spatial or temporal variant data, results of queries, environmental data, and so on. The Wasserstein distance is used to compare two histograms. The Wasserstein distance between histograms is constituted by two components: the first based on the means, and the second, to internal dispersions (standard deviation, skewness, kurtosis, and so on) of the histograms. To cluster sets of histogram data, we propose to use Dynamic Clustering Algorithm, (based on adaptive squared Wasserstein distances) that is a k-means-like algorithm for clustering a set of individuals into $K$ classes that are apriori fixed. The main aim of this research is to provide a tool for clustering histograms, empha...

  15. CCABC: Cyclic Cellular Automata Based Clustering For Energy Conservation in Sensor Networks

    Banerjee, Indrajit; Rahaman, Hafizur

    2011-01-01

    Sensor network has been recognized as the most significant technology for next century. Despites of its potential application, wireless sensor network encounters resource restriction such as low power, reduced bandwidth and specially limited power sources. This work proposes an efficient technique for the conservation of energy in a wireless sensor network (WSN) by forming an effective cluster of the network nodes distributed over a wide range of geographical area. The clustering scheme is developed around a specified class of cellular automata (CA) referred to as the modified cyclic cellular automata (mCCA). It sets a number of nodes in stand-by mode at an instance of time without compromising the area of network coverage and thereby conserves the battery power. The proposed scheme also determines an effective cluster size where the inter-cluster and intra-cluster communication cost is minimum. The simulation results establish that the cyclic cellular automata based clustering for energy conservation in sens...

  16. REMOVING COOL CORES AND CENTRAL METALLICITY PEAKS IN GALAXY CLUSTERS WITH POWERFUL ACTIVE GALACTIC NUCLEUS OUTBURSTS

    Recent X-ray observations of galaxy clusters suggest that cluster populations are bimodally distributed according to central gas entropy and are separated into two distinct classes: cool core (CC) and non-cool core (NCC) clusters. While it is widely accepted that active galactic nucleus (AGN) feedback plays a key role in offsetting radiative losses and maintaining many clusters in the CC state, the origin of NCC clusters is much less clear. At the same time, a handful of extremely powerful AGN outbursts have recently been detected in clusters, with a total energy ∼1061-1062 erg. Using two-dimensional hydrodynamic simulations, we show that if a large fraction of this energy is deposited near the centers of CC clusters, which is likely common due to dense cores, these AGN outbursts can completely remove CCs, transforming them to NCC clusters. Our model also has interesting implications for cluster abundance profiles, which usually show a central peak in CC systems. Our calculations indicate that during the CC to NCC transformation, AGN outbursts efficiently mix metals in cluster central regions and may even remove central abundance peaks if they are not broad enough. For CC clusters with broad central abundance peaks, AGN outbursts decrease peak abundances, but cannot effectively destroy the peaks. Our model may simultaneously explain the contradictory (possibly bimodal) results of abundance profiles in NCC clusters, some of which are nearly flat, while others have strong central peaks similar to those in CC clusters. A statistical analysis of the sizes of central abundance peaks and their redshift evolution may shed interesting insights on the origin of both types of NCC clusters and the evolution history of thermodynamics and AGN activity in clusters.

  17. Customized recommendations for production management clusters of North American automatic milking systems.

    Tremblay, Marlène; Hess, Justin P; Christenson, Brock M; McIntyre, Kolby K; Smink, Ben; van der Kamp, Arjen J; de Jong, Lisanne G; Döpfer, Dörte

    2016-07-01

    Automatic milking systems (AMS) are implemented in a variety of situations and environments. Consequently, there is a need to characterize individual farming practices and regional challenges to streamline management advice and objectives for producers. Benchmarking is often used in the dairy industry to compare farms by computing percentile ranks of the production values of groups of farms. Grouping for conventional benchmarking is commonly limited to the use of a few factors such as farms' geographic region or breed of cattle. We hypothesized that herds' production data and management information could be clustered in a meaningful way using cluster analysis and that this clustering approach would yield better peer groups of farms than benchmarking methods based on criteria such as country, region, breed, or breed and region. By applying mixed latent-class model-based cluster analysis to 529 North American AMS dairy farms with respect to 18 significant risk factors, 6 clusters were identified. Each cluster (i.e., peer group) represented unique management styles, challenges, and production patterns. When compared with peer groups based on criteria similar to the conventional benchmarking standards, the 6 clusters better predicted milk produced (kilograms) per robot per day. Each cluster represented a unique management and production pattern that requires specialized advice. For example, cluster 1 farms were those that recently installed AMS robots, whereas cluster 3 farms (the most northern farms) fed high amounts of concentrates through the robot to compensate for low-energy feed in the bunk. In addition to general recommendations for farms within a cluster, individual farms can generate their own specific goals by comparing themselves to farms within their cluster. This is very comparable to benchmarking but adds the specific characteristics of the peer group, resulting in better farm management advice. The improvement that cluster analysis allows for is

  18. Astrophysics of galaxy clusters

    Ettori, Stefano

    2016-07-01

    As the nodes of the cosmic web, clusters of galaxies trace the large-scale distribution of matter in the Universe. They are thus privileged sites in which to investigate the complex physics of structure formation. However, the complete story of how these structures grow, and how they dissipate the gravitational and non-thermal components of their energy budget over cosmic time, is still beyond our grasp. Most of the baryons gravitationally bound to the cluster's halo is in the form of a diffuse, hot, metal-enriched plasma that radiates primarily in the X-ray band. X-ray observations of the evolving cluster population provide a unique opportunity to address such fundamental open questions as: How do hot diffuse baryons accrete and dynamically evolve in dark matter potentials? How and when was the energy that we observe in the ICM generated and distributed? Where and when are heavy elements produced and how are they circulated? We will present the ongoing activities to define the strategy on how an X-ray observatory with large collecting area and an unprecedented combination of high spectral and angular resolution, such as Athena, can address these questions.

  19. Digital Doping in Magic-Sized CdSe Clusters.

    Muckel, Franziska; Yang, Jiwoong; Lorenz, Severin; Baek, Woonhyuk; Chang, Hogeun; Hyeon, Taeghwan; Bacher, Gerd; Fainblat, Rachel

    2016-07-26

    Magic-sized semiconductor clusters represent an exciting class of materials located at the boundary between quantum dots and molecules. It is expected that replacing single atoms of the host crystal with individual dopants in a one-by-one fashion can lead to unique modifications of the material properties. Here, we demonstrate the dependence of the magneto-optical response of (CdSe)13 clusters on the discrete number of Mn(2+) ion dopants. Using time-of-flight mass spectrometry, we are able to distinguish undoped, monodoped, and bidoped cluster species, allowing for an extraction of the relative amount of each species for a specific average doping concentration. A giant magneto-optical response is observed up to room temperature with clear evidence that exclusively monodoped clusters are magneto-optically active, whereas the Mn(2+) ions in bidoped clusters couple antiferromagnetically and are magneto-optically passive. Mn(2+)-doped clusters therefore represent a system where magneto-optical functionality is caused by solitary dopants, which might be beneficial for future solotronic applications. PMID:27420556

  20. Color Image Segmentation Method Based on Improved Spectral Clustering Algorithm

    Dong Qin

    2014-08-01

    Full Text Available Contraposing to the features of image data with high sparsity of and the problems on determination of clustering numbers, we try to put forward an color image segmentation algorithm, combined with semi-supervised machine learning technology and spectral graph theory. By the research of related theories and methods of spectral clustering algorithms, we introduce information entropy conception to design a method which can automatically optimize the scale parameter value. So it avoids the unstability in clustering result of the scale parameter input manually. In addition, we try to excavate available priori information existing in large number of non-generic data and apply semi-supervised algorithm to improve the clustering performance for rare class. We also use added tag data to compute similar matrix and perform clustering through FKCM algorithms. By the simulation of standard dataset and image segmentation, the experiments demonstrate our algorithm has overcome the defects of traditional spectral clustering methods, which are sensitive to outliers and easy to fall into local optimum, and also poor in the convergence rate

  1. Cluster AAR Campaign Summary Plots

    Fazakerley, A. N.; Walsh, A. P.; Garza, K. J.; Christopher, I.; Sadeghi, S.; Lindqvist, P.; Mihaljcic, B.; Forsyth, C.; Pickett, J. S.; Marklund, G. T.; Lucek, E. A.; Dandouras, I. S.

    2010-12-01

    Since late 2008 the Cluster spacecraft have been making the first four-point measurements of the Auroral Acceleration Region, opening up an exciting new opportunity for the auroral science, Cluster and wider magnetospheric physics communities. In order to stimulate auroral research with Cluster and aid in event selection, we have produced a set of summary plots for those Cluster perigee passes best suited for addressing open questions in auroral physics. The plots incorporate data from WBD, FGM, EFW, PEACE and CIS and are available from the Cluster PEACE website.

  2. Multiscale hierarchical support vector clustering

    Hansen, Michael Saas; Holm, David Alberg; Sjöstrand, Karl; Ley, Carsten Dan; Rowland, Ian John; Larsen, Rasmus

    2008-03-01

    Clustering is the preferred choice of method in many applications, and support vector clustering (SVC) has proven efficient for clustering noisy and high-dimensional data sets. A method for multiscale support vector clustering is demonstrated, using the recently emerged method for fast calculation of the entire regularization path of the support vector domain description. The method is illustrated on artificially generated examples, and applied for detecting blood vessels from high resolution time series of magnetic resonance imaging data. The obtained results are robust while the need for parameter estimation is reduced, compared to support vector clustering.

  3. Electron attachment to HCl clusters

    Negatively charged cluster ions of hydrogen chloride are formed by electron attachment to HCl clusters, which are produced in a seeded supersonic beam traversing a sustained gas discharge. Cluster ions of (HCl)n-, with n = 2, and tentatively with n = 3 and 4 are observed. Cluster ions like Cln-, Cln- (HCl)m, and with Ar attached to them are also seen. The relevance to radiation chemistry of HCl is briefly discussed. Atoms evaporating from the hot, thoriated tungsten filament of the glow discharge lead to clusters such as Thn- and its oxides. (orig.)

  4. Silicon clusters: Chemistry and structure

    Jarrold, M.F.; Ray, U.; Ijiri, Y. (AT and T Bell Labs., Murray Hill, NJ (USA))

    1991-01-01

    The chemical reactions of size selected silicon cluster ions (containing up to 70 atoms) have been studied with a number of different reagents using injected ion drift tube techniques. Both kinetic and equilibrium measurements have been performed as a function of temperature, and the influence of cluster annealing on chemical reactivity explored. Unlike metal clusters, where bulk behavior appears to be approached with around 30 atoms, large silicon clusters (n up to 70) are much less reactive than bulk silicon surfaces. These results suggest that the clusters in the size range examined here are not small crystals of bulk silicon, but have compact, high coordination number structures with few dangling bonds. (orig.).

  5. Cluster headache after orbital exenteration.

    Evers, S; Sörös, P; Brilla, R; Gerding, H; Husstedt, I W

    1997-10-01

    A 37-year-old man developed an ipsilateral headache which fulfilled the criteria for cluster headache after orbital extenteration because of a traumatic lesion of the bulb. The headache could be treated successfully by drugs usually applied in the therapy of cluster headache. Six similar cases of cluster headache after orbital exenteration could be identified in the literature suggesting that the eye itself is not necessarily part of the pathogenesis of cluster headache. We hypothesize that orbital exenteration can cause cluster headache by lesions of sympathetic structures. Possibly, these mechanisms are similar to those of sympathetic reflex dystrophy (Sudeck-Leriche syndrome) causing pain of the limbs. PMID:9350391

  6. Cluster Based Text Classification Model

    Nizamani, Sarwat; Memon, Nasrullah; Wiil, Uffe Kock

    2011-01-01

    We propose a cluster based classification model for suspicious email detection and other text classification tasks. The text classification tasks comprise many training examples that require a complex classification model. Using clusters for classification makes the model simpler and increases the...... classifier is trained on each cluster having reduced dimensionality and less number of examples. The experimental results show that the proposed model outperforms the existing classification models for the task of suspicious email detection and topic categorization on the Reuters-21578 and 20 Newsgroups...... datasets. Our model also outperforms A Decision Cluster Classification (ADCC) and the Decision Cluster Forest Classification (DCFC) models on the Reuters-21578 dataset....

  7. Class in Metropolitan India: The Rise of the Middle Classes

    Mooij, Jos; Lama-Rewal, S.T.

    2009-01-01

    textabstractIt is almost a cliché to say that India’s appearance and image (internationally as well as self-image) have changed dramatically in the last fifteen years. Instead of being associated with rural poverty, India is now associated with high rates of economic growth, a booming Information Technology (IT) sector and, particularly, an increasingly expanding middle class that consumes and behaves like elites and middle classes elsewhere in the world. Considering that cities concentrate m...

  8. Apply Local Clustering Method to Improve the Running Speed of Ant Colony Optimization

    Pang, Chao-Yang; Li, Xia; Hu, Be-Qiong

    2009-01-01

    Ant Colony Optimization (ACO) has time complexity O(t*m*N*N), and its typical application is to solve Traveling Salesman Problem (TSP), where t, m, and N denotes the iteration number, number of ants, number of cities respectively. Cutting down running time is one of study focuses, and one way is to decrease parameter t and N, especially N. For this focus, the following method is presented in this paper. Firstly, design a novel clustering algorithm named Special Local Clustering algorithm (SLC), then apply it to classify all cities into compact classes, where compact class is the class that all cities in this class cluster tightly in a small region. Secondly, let ACO act on every class to get a local TSP route. Thirdly, all local TSP routes are jointed to form solution. Fourthly, the inaccuracy of solution caused by clustering is eliminated. Simulation shows that the presented method improves the running speed of ACO by 200 factors at least. And this high speed is benefit from two factors. One is that class ha...

  9. New Ramsey Classes from Old

    Bodirsky, Manuel

    2012-01-01

    Let C_1 and C_2 be strong amalgamation classes of finite structures, with disjoint finite signatures sigma and tau. Then C_1 wedge C_2 denotes the class of all finite (sigma cup tau)-structures whose sigma-reduct is from C_1 and whose tau-reduct is from C_2. We prove that when C_1 and C_2 are Ramsey, then C_1 wedge C_2 is also Ramsey. We also discuss variations of this statement, and give several examples of new Ramsey classes derived from those general results.

  10. AutoClass: A Bayesian Approach to Classification

    Stutz, John; Cheeseman, Peter; Hanson, Robin; Taylor, Will; Lum, Henry, Jr. (Technical Monitor)

    1994-01-01

    We describe a Bayesian approach to the untutored discovery of classes in a set of cases, sometimes called finite mixture separation or clustering. The main difference between clustering and our approach is that we search for the "best" set of class descriptions rather than grouping the cases themselves. We describe our classes in terms of a probability distribution or density function, and the locally maximal posterior probability valued function parameters. We rate our classifications with an approximate joint probability of the data and functional form, marginalizing over the parameters. Approximation is necessitated by the computational complexity of the joint probability. Thus, we marginalize w.r.t. local maxima in the parameter space. We discuss the rationale behind our approach to classification. We give the mathematical development for the basic mixture model and describe the approximations needed for computational tractability. We instantiate the basic model with the discrete Dirichlet distribution and multivariant Gaussian density likelihoods. Then we show some results for both constructed and actual data.

  11. Hipax Cluster PACS Server

    Ramin Payrovi

    2007-08-01

    Full Text Available Best Performace: With our Hipax Cluster PACS Server solution we are introducing the parallel computing concept as an extremely fast software system to the PACS world. In contrast to the common PACS servers, the Hipax Cluster PACS software is not only restricted to one or two computers, but can be used on a couple of servers controlling each other."nThus, the same services can be run simultaneously on different computers. The scalable system can also be expanded subsequently without lost of per-formance by adding further processors or Hipax server units, for example, if new clients or modalities are to be connected."nMaximum Failure Security: The Cluster Server concept offers high failure security. If one of the server PCs breaks down, the services can be assumed by another Hipax server unit, temporarily. If the overload of one of the server PCs is imminent, the services will be carried out by another Hipax unit (load balancing. To increase the security, e.g. against fire, the single Hipax servers can also be located separately. This concept offers maximum security, flexibility, performance, redundancy and scalability."nThe Hipax Cluster PACS Server is easy to be administrated using a web interface. In the case of a system failure (e.g. overloading, breakdown of a server PC the system administrator receives a mes-sage via Email and is so enabled to solve the problem."nFeatures"n• Based on SQL database"n• Different services running on separate PCs"n• The Hipax Server unis are coordinated and able to control each other"n• Exponentiates the power of a cluster server to the whole PACS (more processors"n• Scalable to the demands"n• Maximum performance"n• Load balancing for optimum efficiency"n• Maximum failure security because of expo-nentiated redundancy"n• Warning Email automatically sent to the system administrator in the case of failure"n• Web interface for system configuration"n• Maintenance without shut down the system

  12. An Analytical Assessment on Document Clustering

    Pushplata; Ram Chatterjee

    2012-01-01

    Clustering is related to data mining for information retrieval. Relevant information is retrieved quickly while doing the clustering of documents. It organizes the documents into groups; each group contains the documents of similar type content. Document clustering is an unsupervised approach of data mining. Different clustering algorithms are used for clustering the documents such as partitioned clustering (K-means Clustering) and Hierarchical Clustering (Agglomerative Hierarchical Clusterin...

  13. A comprehensive approach to mode clustering

    Chen, Yen-Chi; Genovese, Christopher R.; Wasserman, Larry

    2016-01-01

    Mode clustering is a nonparametric method for clustering that defines clusters using the basins of attraction of a density estimator’s modes. We provide several enhancements to mode clustering: (i) a soft variant of cluster assignment, (ii) a measure of connectivity between clusters, (iii) a technique for choosing the bandwidth, (iv) a method for denoising small clusters, and (v) an approach to visualizing the clusters. Combining all these enhancements gives us a complete procedure for cluste...

  14. Web Fuzzy Clustering and a Case Study

    LIU Mao-fu; HE Jing; HE Yan-xiang; HU Hui-jun

    2004-01-01

    We combine the web usage mining and fuzzy clustering and give the concept of web fuzzy clustering, and then put forward the web fuzzy clustering processing model which is discussed in detail. Web fuzzy clustering can be used in the web users clustering and web pages clustering. In the end, a case study is given and the result has proved the feasibility of using web fuzzy clustering in web pages clustering.

  15. Stars in the spherical clusters

    The population of spherical clusters (the old Galaxy objects) is considered on a popular level. The origin of spherical clusters, the process of star enrichment by heavy elements are explained. Presented are the photographs of spherical clusters of the Galaxy, of the Serpent and Berenices Hair constellations. The possible evolutions of spherical cluster stars in the Hertzsprung-RUssel diagram is discussed. Considered is the star lifetime in the main sequence. The branches of red giants, pulsating stars are given. Presented are the Hertzsprung-Russel diagrams for a spherical cluster, marked with a band of instability, and the diagram for M5 cluster in the Serpent constellation obtained from observations. Tracked is the evolution of spherical cluster stars up to the formation of pulsars, white dwarf stars, neutron stars and black holes

  16. Convex clustering: an attractive alternative to hierarchical clustering.

    Gary K Chen

    2015-05-01

    Full Text Available 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/.

  17. The structure of invasion percolation clusters in two dimension

    Z. Daadi-Geev; M. Khaksefidi; Ebrahimi, F.

    2007-01-01

      We have performed extensive numerical simulations to estimate the fractal dimension of the mass and also the anisotropy in the shape of sample spanning cluster (SSC) in 2-D site invasion percolation processes with and without trapping. In agreement with the most recent works, we have observed that these two different processes belong to two different universality classes. Furthermore, we have determined for the first time, the degree of anisotropy in the shape of SSC by evaluating its gyrat...

  18. Atomistic simulations of oleic imidazolines bound to ferric clusters

    Ramachandran, S.; Tsai, B.L.; Blanco, M.; Goddard, W.A. III [California Inst. of Technology, Pasadena, CA (United States); Chen, H.; Tang, Y. [Chevron Petroleum Technology Company, La Habra, CA (United States)

    1997-01-02

    The oleic imidazoline (OI) class of molecules is used extensively for corrosion inhibitor oil field pipeline applications. However, there is no model for understanding how they work. As a first step in elucidating this mechanism we carried out quantum mechanical calculations on clusters involving Fe{sup 3+}, H{sub 2}O, OH, and OI. These calculations are used to determine the MS force field for molecular dynamics simulations. 17 refs., 5 figs., 6 tabs.

  19. Hydrophilic carbon clusters as therapeutic, high capacity antioxidants

    Samuel, Errol L. G.; Duong, MyLinh T.; Bitner, Brittany R.; Marcano, Daniela C.; James M. Tour; Kent, Thomas A

    2014-01-01

    Oxidative stress reflects an excessive accumulation of reactive oxygen species (ROS) and is a hallmark of several acute and chronic human pathologies. While many antioxidants have been investigated, the majority have demonstrated poor efficacy in clinical trials. Here, we discuss limitations of current antioxidants and describe a new class of nanoparticle antioxidants, poly(ethylene glycol)-functionalized hydrophilic carbon clusters (PEG-HCCs). PEG-HCCs show high capacity to annihilate ROS su...

  20. Hydrometeor classification from polarimetric radar measurements: a clustering approach

    Grazioli, J.; D. Tuia; Berne, A.

    2014-01-01

    A data-driven approach to the classification of hydrometeors from measurements collected with polarimetric weather radars is proposed. In a first step, the optimal number nopt of hydrometeor classes that can be reliably identified from a large set of polarimetric data is determined. This is done by means of an unsupervised clustering technique guided by criteria related both to data similarity and to spatial smoothness of the classified images. In a second s...