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Simultaneous clustering: A survey

by Malika Charrad, Mohamed Ben Ahmed - 4th International Conference on Pattern Recognition and Machine Intelligence , 2011
"... Abstract. Although most of the clustering literature focuses on onesided clustering algorithms, simultaneous clustering has recently gained attention as a powerful tool that allows to circumvent some limitations of classical clustering approach. Simultaneous clustering methods perform clustering in ..."
Abstract - Cited by 2 (0 self) - Add to MetaCart
Abstract. Although most of the clustering literature focuses on onesided clustering algorithms, simultaneous clustering has recently gained attention as a powerful tool that allows to circumvent some limitations of classical clustering approach. Simultaneous clustering methods perform clustering

Cluster analysis and display of genome-wide expression patterns’,

by Michael B Eisen , Paul T Spellman , Patrick O Brown , David Botstein - Proc. Natl. Acad. , 1998
"... ABSTRACT A system of cluster analysis for genome-wide expression data from DNA microarray hybridization is described that uses standard statistical algorithms to arrange genes according to similarity in pattern of gene expression. The output is displayed graphically, conveying the clustering and th ..."
Abstract - Cited by 2895 (44 self) - Add to MetaCart
and the underlying expression data simultaneously in a form intuitive for biologists. We have found in the budding yeast Saccharomyces cerevisiae that clustering gene expression data groups together efficiently genes of known similar function, and we find a similar tendency in human data. Thus patterns seen

Simultaneous Clustering and Segmentation for Functional Data

by Bernard Hugueney, Georges Hébrail, Yves Lechevallier, Fabrice Rossi
"... Abstract. We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into K clusters and represents each cluster by a piecewise constant prototype. The total number of segments in the prototypes, P, is chosen by the user and optimally dis ..."
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Abstract. We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into K clusters and represents each cluster by a piecewise constant prototype. The total number of segments in the prototypes, P, is chosen by the user and optimally

Biclustering algorithms for biological data analysis: a survey.

by Sara C Madeira , Arlindo L Oliveira - IEEE/ACM Transactions of Computational Biology and Bioinformatics, , 2004
"... Abstract A large number of clustering approaches have been proposed for the analysis of gene expression data obtained from microarray experiments. However, the results of the application of standard clustering methods to genes are limited. These limited results are imposed by the existence of a num ..."
Abstract - Cited by 481 (15 self) - Add to MetaCart
number of experimental conditions where the activity of genes is uncorrelated. A similar limitation exists when clustering of conditions is performed. For this reason, a number of algorithms that perform simultaneous clustering on the row and column dimensions of the gene expression matrix has been

Clustering Gene Expression Patterns

by Amir Ben-Dor, Ron Shamir, Zohar Yakhini , 1999
"... Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step in the ana ..."
Abstract - Cited by 451 (11 self) - Add to MetaCart
Recent advances in biotechnology allow researchers to measure expression levels for thousands of genes simultaneously, across different conditions and over time. Analysis of data produced by such experiments offers potential insight into gene function and regulatory mechanisms. A key step

Rotation invarient simultaneous clustering and dictionary learning

by Yi-chen Chen, Challa S. Sastry, Vishal M. Patel, P. Jonathon Phillips - in IEEE ICASSP , 2012
"... In this paper, we present an approach that simultaneously clusters database members and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain, while clustering in the image domain. The main feature of the proposed approach is that it provides rotation in ..."
Abstract - Cited by 7 (4 self) - Add to MetaCart
In this paper, we present an approach that simultaneously clusters database members and learns dictionaries from the clusters. The method learns dictionaries in the Radon transform domain, while clustering in the image domain. The main feature of the proposed approach is that it provides rotation

Simultaneous clustering and tracking unknown number of objects

by Katsuhiko Ishiguro, Takeshi Yamada, Naonori Ueda - In Proc. IEEE CVPR , 2008
"... In this paper, we present a novel on-line probabilistic generative model that simultaneously deals with both the clustering and the tracking of an unknown number of moving objects. The proposed model assumes that i) time series data are composed of a time-varying number of objects and that ii) each ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
In this paper, we present a novel on-line probabilistic generative model that simultaneously deals with both the clustering and the tracking of an unknown number of moving objects. The proposed model assumes that i) time series data are composed of a time-varying number of objects and that ii) each

Simultaneous clustering and classification over cluster structure representation

by Qiang Qiana, Songcan Chena, Weiling Caib
"... Two main tasks in pattern recognition area are clustering and classification. Owing to their different goals, traditionally these two tasks are treated sepa-rately. However, when label information is available, such separate treatment can not fully explore data information. First, classification is ..."
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is not favored by the data cluster structure. Second, clustering is not guided by valuable label information. Third, the relationship of clusters and classes is not revealed. Con-trary to this separate learning treatment, simultaneous learning clustering and classification could benefit each other and overcomes

Dryad: Distributed Data-Parallel Programs from Sequential Building Blocks

by Michael Isard, Mihai Budiu, Yuan Yu, Andrew Birrell, Dennis Fetterly - In EuroSys , 2007
"... Dryad is a general-purpose distributed execution engine for coarse-grain data-parallel applications. A Dryad applica-tion combines computational “vertices ” with communica-tion “channels ” to form a dataflow graph. Dryad runs the application by executing the vertices of this graph on a set of availa ..."
Abstract - Cited by 762 (27 self) - Add to MetaCart
simultaneously on multi-ple computers, or on multiple CPU cores within a computer. The application can discover the size and placement of data at run time, and modify the graph as the computation pro-gresses to make efficient use of the available resources. Dryad is designed to scale from powerful multi-core sin

Information-Theoretic Co-Clustering

by Inderjit S. Dhillon, Subramanyam Mallela, Dharmendra S. Modha - In KDD , 2003
"... Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingency table analysis is co-clustering: simultaneous clustering of the rows and columns. A novel theoretical formulation views ..."
Abstract - Cited by 346 (12 self) - Add to MetaCart
Two-dimensional contingency or co-occurrence tables arise frequently in important applications such as text, web-log and market-basket data analysis. A basic problem in contingency table analysis is co-clustering: simultaneous clustering of the rows and columns. A novel theoretical formulation
Results 1 - 10 of 3,460