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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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Submitted on : Wednesday, May 24, 2006 - 12:03:57 PM
Last modification on : Friday, February 4, 2022 - 3:29:49 AM
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  • HAL Id : inria-00073175, version 1



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



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