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[Sap17c] Clusterwise methods, past and present
Conférence Internationale avec comité de lecture : 61 World Statistics Congress, July 2017, pp.xx, Marrakech, Maroc,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.
BibTeX
| @inproceedings { | |||
| Sap17c, | |||
| title | = | "{Clusterwise methods, past and present}", | |
| author | = | " G. Saporta ", | |
| booktitle | = | "{61 World Statistics Congress}", | |
| year | = | 2017, | |
| month | = | "July", | |
| pages | = | "xx", | |
| address | = | "Marrakech, Maroc", | |
| } | |||
