Block clustering with Bernoulli mixture models: Comparison of different approaches

Authors:
Gérard Govaert;Mohamed Nadif
Affiliations:
UMR 6599, CNRS & Universitéé de Technologie de Compiègne, 60205 Compiègne, France;CRIP5, Université Paris Descartes, 45 rue des Saint-Pères, 75260 Paris, France
Venue:
Computational Statistics & Data Analysis
Year:
2008

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Abstract

The block or simultaneous clustering problem on a set of objects and a set of variables is embedded in the mixture model. Two algorithms have been developed: block EM as part of the maximum likelihood and fuzzy approaches, and block CEM as part of the classification maximum likelihood approach. A unified framework for obtaining different variants of block EM is proposed. These variants are studied and their performances evaluated in comparison with block CEM, two-way EM and two-way CEM, i.e EM and CEM applied separately to the two sets.