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Clusterwise methods, past and present

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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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Dates and versions

hal-02473529 , version 1 (10-02-2020)

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  • HAL Id : hal-02473529 , version 1

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