Model - based clustering of functional data ∗
نویسندگان
چکیده
Model-based clustering for functional data is considered. An alternative to model-based clustering using the functional principal components is proposed by approximating the density of functional random variables. The EM algorithm is used for parameter estimation and the maximum a posteriori rule provides the clusters. Simulation study and real data application illustrate the interest of the proposed methodology. Résumé Ce papier traite de la classi cation automatique de données fonctionnelles. Nous proposons une procédure à base de modèles de mélange, dénis à partir d'une approximation de la notion de densité d'une variable aléatoire fonctionnelle. L'estimation des paramètres par maximum de vraisemblance est réalisée à l'aide de l'algorithme EM, et la classi cation est réalisée par maximum a posteriori. Des études sur simulations et données réelles illustrent l'intérêt de la méthodologie proposée en comparaison des approches classiques. MSC 2009 subject classi cations. 62H30, 62H25, 62M10.
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