Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

Abstract : We propose in this paper an exploratory analysis algorithm for functional data. The method partitions a set of functions into $K$ clusters and represents each cluster by a simple prototype (e.g., piecewise constant). The total number of segments in the prototypes, $P$, is chosen by the user and optimally distributed among the clusters via two dynamic programming algorithms. The practical relevance of the method is shown on two real world datasets.
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Neurocomputing / EEG Neurocomputing, Elsevier, 2010, 73 (7-9), pp.Pages 1125-1141. <10.1016/j.neucom.2009.11.022>
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https://hal.archives-ouvertes.fr/hal-00515908
Contributeur : Fabrice Rossi <>
Soumis le : mercredi 8 septembre 2010 - 11:29:37
Dernière modification le : jeudi 9 février 2017 - 15:19:44

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Georges Hébrail, Bernard Hugueney, Yves Lechevallier, Fabrice Rossi. Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation. Neurocomputing / EEG Neurocomputing, Elsevier, 2010, 73 (7-9), pp.Pages 1125-1141. <10.1016/j.neucom.2009.11.022>. <hal-00515908>

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