Model-based Cluster and Discriminant Analysis with the MIXMOD software

Christophe Biernacki (1) , Gilles Celeux (2) , Gérard Govaert (3) , Florent Langrognet (4)

1 LPP - Laboratoire Paul Painlevé - UMR 8524
2 SELECT - Model selection in statistical learning
3 Heudiasyc - Heuristique et Diagnostic des Systèmes Complexes [Compiègne]
4 LMB - Laboratoire de Mathématiques de Besançon (UMR 6623)

The Mixture Modeling (MIXMOD) program fits mixture models to a given data set for the purposes of density estimation, clustering or discriminant analysis. A large variety of algorithms to estimate the mixture parameters are proposed (EM, Classification EM, Stochastic EM), and it is possible to combine these to yield different strategies for obtaining a sensible maximum for the likelihood (or complete-data likelihood) function. MIXMOD is currently intended to be used for multivariate Gaussian mixtures, and fourteen different Gaussian models can be distinguished according to different assumptions regarding the component variance matrix eigenvalue decomposition. Moreover, different information criteria for choosing a parsimonious model (the number of mixture components, for instance) are included, their suitability depending on the particular perspective (cluster analysis or discriminant analysis). Written in C++, MIXMOD is interfaced with SCILAB and MATLAB. The program, the statistical documentation and the user guide are available on the internet at the following address: http://www-math.univ-fcomte.fr/mixmod/index.php.

Mots clés: Gaussian models; EM-like algorithms; Model selection

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Christophe Biernacki, Gilles Celeux, Gérard Govaert, Florent Langrognet. Model-based Cluster and Discriminant Analysis with the MIXMOD software. Computational Statistics and Data Analysis, 2006, 51, pp.587-600. ⟨hal-00469477⟩