PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Model-based cluster analysis and discriminant analysis with the MIXMOD software
Christophe Biernacki, Gilles Celeux, Gérard Govaert and Florent Langrognet
Computational Statistitics and Data Analysis 2005.

Abstract

The mixmod (mixture modelling) software fits mixture models to a given data set with a density estimation, a clustering or a discriminant analysis purpose. A large variety of algorithms to estimate the mixture parameters are proposed (EM, Classification EM, Stochastic EM) and it is possible to combine them to lead to different strategies in order to get a sensible maximum of the likelihood (or complete-data likelihood) function. mixmod is currently focused on multivariate Gaussian mixtures and fourteen different Gaussian models can be considered according to different assumptions on the component variance matrix eigenvalue decomposition. Moreover, different information criteria for choosing a parsimonious model (the number of mixture components, for instance), some of them favoring either a cluster analysis or a discriminant analysis view point, are included. Written in C++, mixmod is interfaced with Scilab and Matlab. The software, the statistical documentation and also the user guide are available on the internet at the following address: http://www-math.univ-fco mte.fr/mixmod/index.php.

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EPrint Type:Article
Additional Information:Software presentation
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
ID Code:1851
Deposited By:Gilles Celeux
Deposited On:29 November 2005