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Conference papers

Clusterwise methods, past and present

Gilbert Saporta 1
1 CEDRIC - MSDMA - CEDRIC. Méthodes statistiques de data-mining et apprentissage
CEDRIC - Centre d'études et de recherche en informatique et communications
Abstract : 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.
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Submitted on : Monday, February 10, 2020 - 5:37:13 PM
Last modification on : Monday, February 21, 2022 - 3:38:18 PM


  • HAL Id : hal-02473529, version 1



Gilbert Saporta. Clusterwise methods, past and present. ISI 2017 61st World Statistics Congress, Jul 2017, Marrakech, Morocco. ⟨hal-02473529⟩



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