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Reich Brian J BJ Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695, USA.   2011
Identifying homogeneous groups of individuals is an important problem in population genetics. Recently, several methods have been proposed that exploit spatial information to improve clustering algorithms. In this article, we develop a Bayesian clustering algorithm based on the Dirichlet process prior that uses both genetic and spatial information to classify ...


Li Xiaoyun X Department of Statistics, Florida State University, Tallahassee, Florida 32306, USA.   2011
In some biomedical studies involving clustered binary responses (say, disease status), the cluster sizes can vary because some components of the cluster can be absent. When both the presence of a cluster component as well as the binary disease status of a present component are treated as responses of interest, ...


Hammer Barbara   2010
Topographic maps such as the selforganizing map (SOM) or neural gas (NG) constitute powerful data mining techniques that allow simultaneously clustering data and inferring their topological structure, such that additional features, for example, browsing, become available. Both methods have been introduced for vectorial data sets; they require a classical feature ...


Cook Andrea J AJ Biostatistics Unit, Group Health Research Institute, Seattle, Washington 98101, USA.   2010
Spatial cluster detection is an important methodology for identifying regions with excessive numbers of adverse health events without making strong model assumptions on the underlying spatial dependence structure. Previous work has focused on point or individuallevel outcome data and few advances have been made when the outcome data are reported ...


Guo Jian J Department of Statistics, University of Michigan, Ann Arbor, Michigan 48109,   2010
Variable selection for clustering is an important and challenging problem in highdimensional data analysis. Existing variable selection methods for modelbased clustering select informative variables in a "oneinallout" manner; that is, a variable is selected if at least one pair of clusters is separable by this variable and removed if it ...


Zhang Kai   2010
Kernel (or similarity) matrix plays a key role in many machine learning algorithms such as kernel methods, manifold learning, and dimension reduction. However, the cost of storing and manipulating the complete kernel matrix makes it infeasible for large problems. The Nyström method is a popular samplingbased lowrank approximation scheme for ...


Mimaroglu Selim   2010
Clustering methods including kmeans, SOM, UPGMA, DAA, CLICK, GENECLUSTER, CAST, DHC, PMETIS and KMETIS have been widely used in biological studies for gene expression, protein localization, sequence recognition and more. All these clustering methods have some benefits and drawbacks. We propose a novel graphbased clustering software called COMUSA for combining ...


Zhong Wenliang   2010
Clustering using the Hilbert Schmidt independence criterion (CLUHSIC) is a recent clustering algorithm that maximizes the dependence between cluster labels and data observations according to the Hilbert Schmidt independence criterion (HSIC). It is unique in that structure information on the cluster outputs can be easily utilized in the clustering process. ...


Yeginer Mete   2010
The objective of this study is to probe the existence of a third crackle type, medium, besides the traditionally accepted types, namely, fine and coarse crackles and, furthermore, to explore the representative parameter values for each crackle type. A set of clustering experiments have been conducted on pulmonary crackles to ...


Harshe Yogesh M   2010
The hydrodynamic properties of rigid fractal aggregates are key ingredients in understanding the governing mechanism of their motion and the properties of their suspensions. In the present work we outline explicit equations for the estimation of the complete set of hydrodynamic properties of arbitrary shaped aggregates made of uniform sized ...


Brumback Babette A   2010
In social epidemiology, one often considers neighborhood or contextual effects on health outcomes, in addition to effects of individual exposures. This paper is concerned with the estimation of an individual exposure effect in the presence of confounding by neighborhood effects, motivated by an analysis of National Health Interview Survey (NHIS) ...


Jung Inkyung I Department of Biostatistics, Yonsei University College of Medicine, Seoul, Korea.   2010
As a geographical cluster detection analysis tool, the spatial scan statistic has been developed for different types of data such as Bernoulli, Poisson, ordinal, exponential and normal. Another interesting data type is multinomial. For example, one may want to find clusters where the diseasetype distribution is statistically significantly different from ...


Galbraith Sally   2010
Statistical analysis is critical in the interpretation of experimental data across the life sciences, including neuroscience. The nature of the data collected has a critical role in determining the best statistical approach to take. One particularly prevalent type of data is referred to as "clustered data." Clustered data are characterized ...


Hausdorf Bernhard   2010
We propose a method for delimiting species based on dominant or codominant multilocus data using Gaussian clustering with a noise component for outliers. Case studies show that provisional species delimited using Gaussian clustering based on dominant multilocus data correspond well with provisional species delimited based on other data. However, the ...


Keilholz Shella D SD Wallace H. Coulter Department of Biomedical Engineering, Emory University/Georgia Institute of Technology, 101 Woodruff Circle, Suite 2001, Atlanta, GA 30322, USA.   2010
Correlated low frequency fluctuations in the blood oxygenation level dependent signal have been widely observed in highly connected brain regions and are considered to be indicative of coordinated activity within those regions. A typical functional connectivity MRI study consists of hundreds of time points acquired from thousands of image voxels, ...


Wang Xiaogang X Department of Electronic Engineering, the Chinese University of Hong Kong, Hong Kong.   2011
In this paper, we propose a new nonparametric Bayesian framework to cluster white matter fiber tracts into bundles using a hierarchical Dirichlet processes mixture (HDPM) model. The number of clusters is automatically learned driven by data with a Dirichlet process (DP) prior instead of being manually specified. After the models ...


Visser Eelke   2011
We describe a novel scalable clustering framework for streamlines obtained from diffusion tractography. Clustering is an attractive means of segmenting a large set of streamlines into anatomically relevant bundles. For most existing methods, however, the large datasets produced in high resolution or multiple subject studies are problematical. To achieve good ...


Gu Qiong   2010
The quality of diverse compound selection mainly depends on cluster algorithms, descriptors, the combinations of the descriptors, and similarity metrics. The JarvisPatrick algorithm, MDL search keys, and Daylight fingerprints are a well accepted algorithm and structure descriptors for compound library diversity analysis. Based upon our 288 experiments on selecting compounds ...


Follmann Dean   2010
This paper introduces exact permutation methods for use when there are independent clusters of data with arbitrary withincluster correlation. To eliminate the problem of clustering, we randomly select a data point from each cluster and for this now independent data, and calculate our test statistic and the associated support points ...


Philippe T   2010
The statistical 1NN method is an elegant way to derive the composition of small Benriched clusters in a random AB solid solution from 3D atomic fields. An extension of this method is proposed that includes the contribution of interface region and provides an estimate of the core composition of clusters. ...


Marin Stefania   2010
This study systematically investigates the temporal organization of American English onset and coda consonant clusters on the basis of kinematic data. Results from seven speakers suggest that consonants in complex onsets are organized globally with respect to the following vowel, while consonants in complex codas are organized locally relative to ...


Kouam Marc K   2010
Maximum entropy ecological niche modeling and spatial scan statistic were utilized to predict the geographic range and to investigate clusters of infections for equine piroplasms in Greece, using the Maxent and SaTScan programs, respectively. The eastern half of the country represented the culminating area with high probabilities (p>0.75) of presence ...


Candel Math J J M   2010
Adjustments of sample size formulas are given for varying cluster sizes in cluster randomized trials with a binary outcome when testing the treatment effect with mixed effects logistic regression using secondorder penalized quasilikelihood estimation (PQL). Starting from firstorder marginal quasilikelihood (MQL) estimation of the treatment effect, the asymptotic relative efficiency ...


Rossi Giulia   2010
The structures of Ni/MgO nanoparticles are studied by means of global optimization searches. The results from four different model potentials, sharing the same functional forms but different parametrizations, are reported and compared. Two parametrizations over four give qualitatively correct results, and one of them is also quantitatively satisfactory. The other ...


Jansen Ryan   2010
A computational approach capable of modeling homogeneous condensation in nonequilibrium environments is presented. The approach is based on the direct simulation Monte Carlo (DSMC) method, extended as appropriate to include the most important processes of cluster nucleation and evolution at the microscopic level. The approach uses a recombinationreaction energydependent mechanism ...


Nandy Subhajit   2010
We present a genetic algorithm based investigation of structural fragmentation in dicationic noble gas clusters, Ar(n)(+2), Kr(n)(+2), and Xe(n)(+2), where n denotes the size of the cluster. Dications are predicted to be stable above a threshold size of the cluster when positive charges are assumed to remain localized on two ...


Tavares J M JM Instituto Superior de Engenharia de Lisboa, P1950062 Lisbon,   2010
We calculate the equilibrium thermodynamic properties, percolation threshold, and cluster distribution functions for a model of associating colloids, which consists of hard spherical particles having on their surfaces three shortranged attractive sites (sticky spots) of two different types, A and B. The thermodynamic properties are calculated using Wertheim's perturbation theory ...


Southern Richard   2010
A leastsquares mesh is a surface representation consisting of a small set of anchor points and the differential and topological properties of the surface. In this paper we present a novel method to identify motion sensitive anchor points for leastsquares meshes from a set of examples. We present a new ...


Levison Harold F   2010
Oort cloud comets are currently believed to have formed in the Sun's protoplanetary disk and to have been ejected to large heliocentric orbits by the giant planets. Detailed models of this process fail to reproduce all of the available observational constraints, however. In particular, the Oort cloud appears to be ...


Keizer Ron J   2011
Pharmacokineticpharmacodynamic modeling using nonlinear mixed effects modeling (NONMEM) is a powerful yet challenging technique, as the software is generally accessed from the command line. A graphical user interface, Piraña, was developed that offers a complete modeling environment for NONMEM, enabling both novice and advanced users to increase efficiency of their ...


Furtlehner Cyril   2010
We analyze and exploit some scaling properties of the affinity propagation (AP) clustering algorithm proposed by Frey and Dueck [Science 315, 972 (2007)]. Following a divide and conquer strategy we setup an exact renormalizationbased approach to address the question of clustering consistency, in particular, how many cluster are present in ...


Mérigot Bastien   2010
Clustering methods are widely used tools in many aspects of science, such as ecology, medicine, or even market research, that commonly deal with dendrogrambased analyses. In such analyses, for a given initial dissimilarity matrix, the resulting dendrogram may strongly vary according to the selected clustering methods. However, numerous dendrogrambased analyses ...


Zachar Peter   2010
Cramer et al.'s critique of latent variables implicitly advocates a type of scientific antirealism which can be extended to many dispositional constructs in scientific psychology. However, generalizing Cramer et al.'s network model in this way raises concerns about its applicability to psychopathology. The model could be improved by articulating why ...


Witten Daniela M   2010
We consider the problem of clustering observations using a potentially large set of features. One might expect that the true underlying clusters present in the data differ only with respect to a small fraction of the features, and will be missed if one clusters the observations using the full set ...


Nasibov Efendi   2010
Abstract Among various types of clustering methods, partitionbased methods such as kmeans and FCM are widely used in the analysis of such data. However, when duration between stimuli is different, such methods are not able to provide satisfactory results because they find equal size clusters according to the fundamental running ...


Baudry JeanPatrick   2010
Modelbased clustering consists of fitting a mixture model to data and identifying each cluster with one of its components. Multivariate normal distributions are typically used. The number of clusters is usually determined from the data, often using BIC. In practice, however, individual clusters can be poorly fitted by Gaussian distributions, ...


Dua Sumeet   2011
Medical sciences are rapidly emerging as a data rich discipline where the amount of databases and their dimensionality increases exponentially with time. Data integration algorithms often rely upon discovering embedded, useful, and novel relationships between feature attributes that describe the data. Such algorithms require data integration prior to knowledge discovery, ...


Iamon Natthakan   2010
MOTIVATION: It is far from trivial to select the most effective clustering method and its parameterization, for a particular set of gene expression data, because there are a very large number of possibilities. Although many researchers still prefer to use hierarchical clustering in one form or another, this is often ...


Chatzis Sotirios P   2010
Hidden Markov random field (HMRF) models are widely used for image segmentation, as they appear naturally in problems where a spatially constrained clustering scheme is asked for. A major limitation of HMRF models concerns the automatic selection of the proper number of their states, i.e., the number of region clusters ...


Isenburg Martin   2010
A hybrid parallel and outofcore algorithm pads blocks from a structured grid with layers of ghost data from adjacent blocks. This enables endtoend streaming computations on very large data sets that gracefully adapt to available computing resources, from a singleprocessor machine to parallel visualization clusters.


Abellan van Kan Gabor   2010
No clear consensual definition regarding frailty seems to emerge from the literature after 30 years of research in the topic, and a large array of models and criteria has been proposed to define the syndrome. Controversy continues to exist on the choice of the components to be included in the ...


Wilkerson Matthew D   2010
Unsupervised class discovery is a highly useful technique in cancer research, where intrinsic groups sharing biological characteristics may exist but are unknown. The consensus clustering (CC) method provides quantitative and visual stability evidence for estimating the number of unsupervised classes in a dataset. ConsensusClusterPlus implements the CC method in R ...


Boongoen Tossapon   2010
The intuition of data reliability has recently been incorporated into the main stream of research on ordered weighted averaging (OWA) operators. Instead of relying on humanguided variables, the aggregation behavior is determined in accordance with the underlying characteristics of the data being aggregated. Dataoriented operators such as the dependent OWA ...


Yang Yi   2010
In this paper, we propose a new image clustering algorithm, referred to as clustering using local discriminant models and global integration (LDMGI). To deal with the data points sampled from a nonlinear manifold, for each data point, we construct a local clique comprising this data point and its neighboring data ...


BhaskaranNair Kiran   2010
We have formulated the multireference Mukherjee's coupled clusters method with triexcitations (MR MkCCSDT) in the linked version and implemented it in the ACES II program package. The assessment of the new method has been performed on the first three electronic states of the oxygen molecule, on studies of singlettriplet gap ...


Hu W W School of Population Health, The University of Queensland, Australia.   2011
This study aimed to investigate the spatial clustering and dynamic dispersion of dengue incidence in Queensland, Australia. We used Moran's I statistic to assess the spatial autocorrelation of reported dengue cases. Spatial empirical Bayes smoothing estimates were used to display the spatial distribution of dengue in postal areas throughout Queensland. ...


Cuesta I García   2010
Gauge origin independent calculations of nuclear magnetic shielding tensors are carried out inside the formalism of the continuous transformation of the origin of the current density leading to formal annihilation of its diamagnetic contribution (CTOCDDZ). We employ the unrelaxed linear response approach with a hierarchy of different coupled cluster methods ...


Chiosa Iurie   2010
The processing power of parallel coprocessors like the Graphics Processing Unit (GPU) are dramatically increasing. However, up until now only a few approaches have been presented to utilize this kind of hardware for mesh clustering purposes. In this paper we introduce a Multilevel clustering technique designed as a parallel algorithm ...


Zheng HaiTao   2010
OBJECTIVE: Biomedical document conceptualization is the process of clustering biomedical documents based on ontologyrepresented domain knowledge. The result of this process is the representation of the biomedical documents by a set of key concepts and their relationships. Most of clustering methods cluster documents based on invariant domain knowledge. The objective ...


Tiwari Hemant K   2010
Hoffman et al. [1] proposed an elegant resampling method for analyzing clustered binary data. The focus of their paper was to perform association tests on clustered binary data using withinclusterresampling (WCR) method. Follmann et al. [2] extended Hoffman et al.'s procedure more generally with applicability to angular data, combining of ...


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