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
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.
Type de document :
Rapport
RR-3509, INRIA. 1998
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https://hal.inria.fr/inria-00073175
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Soumis le : mercredi 24 mai 2006 - 12:03:57
Dernière modification le : jeudi 19 avril 2018 - 14:49:43
Document(s) archivé(s) le : dimanche 4 avril 2010 - 21:07:48

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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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