Learning inverted dirichlet mixtures for positive data clustering

Authors:
Taoufik Bdiri;Nizar Bouguila
Affiliations:
Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada, Qc;Concordia Institute for Information Systems Engineering, Concordia University, Montreal, Canada, Qc
Venue:
RSFDGrC'11 Proceedings of the 13th international conference on Rough sets, fuzzy sets, data mining and granular computing
Year:
2011

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Abstract

In this paper, we propose a statistical model to cluster positive data. The proposed model adopts a mixture of inverted Dirichlet distributions and is learned using expectation-maximization (EM) for parameters estimation and the minimum message length criterion (MML) for model selection. Experimental results using both synthetic and real data are presented to show the advantages of the proposed model.