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Assessing a Mixture Model for Clustering with the Integrated Classification Likelihood

Christophe Biernacki 1 Gilles Celeux Gérard Govaert
1 IS2 - Statistical Inference for Industry and Health
Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
Abstract : We propose assessing a mixture model in a cluster analysis setting with the inegrated classification likelihood. With this purpose, the observed data are assigned to unknown clusters using a maximum a posteriori operator. The integrated completed likelihood approximation is derived without the theoretical difficulties encountered when approximating the integrated observed likelihood. Numerical experiments on simulated and real data of the resulting ICL criterion show that it performs well both for choosing a mixture model and a relevant number of clusters. In particular, ICL appears to be more robust than BIC to violation of some of the mixture model assumptions and it can select a number of clusters leading to a sensible partitioning of the data.
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https://hal.inria.fr/inria-00073163
Contributor : Rapport de Recherche Inria <>
Submitted on : Wednesday, May 24, 2006 - 12:01:58 PM
Last modification on : Monday, February 10, 2020 - 4:36:45 PM
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  • HAL Id : inria-00073163, version 1

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Christophe Biernacki, Gilles Celeux, Gérard Govaert. Assessing a Mixture Model for Clustering with the Integrated Classification Likelihood. RR-3521, INRIA. 1998. ⟨inria-00073163⟩

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