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On multivariate binary data clustering and feature weighting

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  • Bouguila, Nizar

Abstract

This paper presents an approach that partitions data sets of unlabeled binary vectors without a priori information about the number of clusters or the saliency of the features. The unsupervised binary feature selection problem is approached using finite mixture models of multivariate Bernoulli distributions. Using stochastic complexity, the proposed model determines simultaneously the number of clusters in a given data set composed of binary vectors and the saliency of the features used. We conduct different applications involving real data, document classification and images categorization to show the merits of the proposed approach.

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  • Bouguila, Nizar, 2010. "On multivariate binary data clustering and feature weighting," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 120-134, January.
    • Handle: RePEc:eee:csdana:v:54:y:2010:i:1:p:120-134
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    References listed on IDEAS

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    5. J. D. Wilbur & J. K. Ghosh & C. H. Nakatsu & S. M. Brouder & R. W. Doerge, 2002. "Variable Selection in High-Dimensional Multivariate Binary Data with Application to the Analysis of Microbial Community DNA Fingerprints," Biometrics, The International Biometric Society, vol. 58(2), pages 378-386, June.
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    7. Gilles Celeux & Gérard Govaert, 1991. "Clustering criteria for discrete data and latent class models," Journal of Classification, Springer;The Classification Society, vol. 8(2), pages 157-176, December.
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    1. Guillaume Gautreau & Adelme Bazin & Mathieu Gachet & Rémi Planel & Laura Burlot & Mathieu Dubois & Amandine Perrin & Claudine Médigue & Alexandra Calteau & Stéphane Cruveiller & Catherine Matias & Chr, 2020. "PPanGGOLiN: Depicting microbial diversity via a partitioned pangenome graph," PLOS Computational Biology, Public Library of Science, vol. 16(3), pages 1-27, March.

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