[Sap17c] Clusterwise methods, past and present

Conférence Internationale avec comité de lecture : 61 World Statistics Congress, July 2017, pp.xx, Marrakech, Maroc,

Auteurs: G. Saporta

Mots clés: clusterwise regression; mixture models; dimension reduction; PLS regression

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.


@inproceedings {
title="{Clusterwise methods, past and present}",
author=" G. Saporta ",
booktitle="{61 World Statistics Congress}",
address="Marrakech, Maroc",