Clusterwise methods, past and present
Résumé
Instead of fitting a single and global model (regression, PCA, etc.) to a set of observations, clusterwise methods look simultaneously for a partition into k clusters and k local models optimizing some criterion. There are two main approaches: 1. the least squares approach introduced by E.Diday in the 70's, derived from k-means 2. mixture models using maximum likelihood but only the first one easily enables prediction. After a survey of classical methods, we will present recent extensions to functional, symbolic and multiblock data.
Domaines
Statistiques [stat]
Fichier principal
Paper-Saporta_ISI2017.pdf (423.84 Ko)
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Clusterwise_Marrakech_2017_Saporta.pdf (1.82 Mo)
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