Block clustering with Bernoulli mixture models: Comparison of different approaches

Gérard Govaert and Mohamed Nadif

Computational Statistics & Data Analysis, 2008, vol. 52, issue 6, 3233-3245

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.

Date: 2008
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