PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Block clustering with Bernoulli mixture models: Comparison of different approaches
Gérard Govaert and Mohamed Nadif
Computational Statistics and Data Analysis Volume 52, Number 6, pp. 3233-3245, 2008.

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.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:6378
Deposited By:Gérard Govaert
Deposited On:08 March 2010