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Choosing Models in Model-based Clustering and Discriminant Analysis

Christophe Biernacki 1 Gérard Govaert
1 IS2 - Statistical Inference for Industry and Health
Inria Grenoble - Rhône-Alpes, LBBE - Laboratoire de Biométrie et Biologie Evolutive - UMR 5558
Abstract : Using an eigenvalue decomposition of variance matrices, Celeux and Govaert (1993) obtained numerous and powerful models for Gaussian model-based clustering and discriminant analysis. Through Monte Carlo simulations, we compare the performances of many classical criteria to select these models: information criteria as AIC, the Bayesian criterion BIC, classification criteria as NEC and cross-validation. In the clustering context, information criteria and BIC outperform the classification criteria. In the discriminant analysis context, cross-validation shows good performance but information criteria and BIC give satisfactory results as well with, by far, less time-computing.
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https://hal.inria.fr/inria-00073175
Contributor : Rapport de Recherche Inria <>
Submitted on : Wednesday, May 24, 2006 - 12:03:57 PM
Last modification on : Monday, February 10, 2020 - 4:36:45 PM
Long-term archiving on: : Sunday, April 4, 2010 - 9:07:48 PM

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  • HAL Id : inria-00073175, version 1

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Christophe Biernacki, Gérard Govaert. Choosing Models in Model-based Clustering and Discriminant Analysis. RR-3509, INRIA. 1998. ⟨inria-00073175⟩

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