Model-based Clustering of Time Series in Group-specific Functional Subspaces

Charles Bouveyron 1 Julien Jacques 2, 3
3 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Inria Lille - Nord Europe, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille, Université de Lille 1, IUT’A
Abstract : This work develops a general procedure for clustering functional data which adapts the efficient clustering method HDDC, originally proposed in the multivariate context. The resulting clustering method, called funHDDC, is based on a functional latent mixture model which fits the functional data in group-specific functional subspaces. By constraining model parameters within and between groups, a family of parsimonious models is exhibited which allow to fit onto various situations. An estimation procedure based on the EM algorithm is proposed for estimating both the model parameters and the group-specific functional subspaces. Experiments on real-world datasets show that the proposed approach performs better or similarly than classical clustering methods while providing useful interpretations of the groups.
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Advances in Data Analysis and Classification, Springer Verlag, 2011, 5 (4), pp.281-300
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Charles Bouveyron, Julien Jacques. Model-based Clustering of Time Series in Group-specific Functional Subspaces. Advances in Data Analysis and Classification, Springer Verlag, 2011, 5 (4), pp.281-300. 〈hal-00559561v2〉

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