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Block clustering with Bernoulli mixture models: comparison of different approaches. (English) Zbl 1452.62444

Summary: 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.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62-08 Computational methods for problems pertaining to statistics
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