Model-based cluster and discriminant analysis with the MIXMOD software. 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. (Source: https://www.projet-plume.org/en/relier/mixmod)

References in zbMATH (referenced in 37 articles )

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  1. Cristina Tortora, Ryan P. Browne, Aisha ElSherbiny, Brian C. Franczak, Paul D. McNicholas: Model-Based Clustering, Classification, and Discriminant Analysis Using the Generalized Hyperbolic Distribution: MixGHD R package (2021) not zbMATH
  2. Polanski, Andrzej; Marczyk, Michal; Pietrowska, Monika; Widlak, Piotr; Polanska, Joanna: Initializing the EM algorithm for univariate Gaussian, multi-component, heteroscedastic mixture models by dynamic programming partitions (2018)
  3. Nakamura, Thiago A.; Palhares, Reinaldo M.; Caminhas, Walmir M.; Menezes, Benjamin R.; de Campos, Mário Cesar M. M.; Fumega, Ubirajara; de M. Bomfim, Carlos H.; Lemos, André P.: Adaptive fault detection and diagnosis using parsimonious Gaussian mixture models trained with distributed computing techniques (2017)
  4. Bongiorno, Enea G.; Goia, Aldo: Classification methods for Hilbert data based on surrogate density (2016)
  5. McNicholas, Paul D.: Model-based clustering (2016)
  6. Baudry, Jean-Patrick; Cardoso, Margarida; Celeux, Gilles; Amorim, Maria José; Ferreira, Ana Sousa: Enhancing the selection of a model-based clustering with external categorical variables (2015)
  7. Gallopin, Mélina; Celeux, Gilles; Jaffrézic, Florence; Rau, Andrea: A model selection criterion for model-based clustering of annotated gene expression data (2015)
  8. Rémi Lebret; Serge Iovleff; Florent Langrognet; Christophe Biernacki; Gilles Celeux; Gérard Govaert: Rmixmod: The R Package of the Model-Based Unsupervised, Supervised, and Semi-Supervised Classification Mixmod Library (2015) not zbMATH
  9. Riani, Marco; Cerioli, Andrea; Perrotta, Domenico; Torti, Francesca: Simulating mixtures of multivariate data with fixed cluster overlap in FSDA library (2015)
  10. Scrucca, Luca; Raftery, Adrian E.: Improved initialisation of model-based clustering using Gaussian hierarchical partitions (2015)
  11. Zhao, Jianhua; Jin, Libin; Shi, Lei: Mixture model selection via hierarchical BIC (2015)
  12. Andrews, Jeffrey L.; McNicholas, Paul D.: Variable selection for clustering and classification (2014)
  13. Biernacki, Christophe; Lourme, Alexandre: Stable and visualizable Gaussian parsimonious clustering models (2014)
  14. Bouveyron, Charles; Brunet-Saumard, Camille: Model-based clustering of high-dimensional data: a review (2014)
  15. Browne, Ryan P.; McNicholas, Paul D.: Orthogonal Stiefel manifold optimization for eigen-decomposed covariance parameter estimation in mixture models (2014)
  16. Browne, Ryan P.; McNicholas, Paul D.: Estimating common principal components in high dimensions (2014)
  17. Galimberti, Giuliano; Soffritti, Gabriele: A multivariate linear regression analysis using finite mixtures of (t) distributions (2014)
  18. Gollini, Isabella; Murphy, Thomas Brendan: Mixture of latent trait analyzers for model-based clustering of categorical data (2014)
  19. Lin, Tsung-I: Learning from incomplete data via parameterized (t) mixture models through eigenvalue decomposition (2014)
  20. Galimberti, Giuliano; Soffritti, Gabriele: Using conditional independence for parsimonious model-based Gaussian clustering (2013)

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Further publications can be found at: http://www.mixmod.org/article.php3?id_article=16