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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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https://hal.archives-ouvertes.fr/hal-00515908
Contributor : Fabrice Rossi <>
Submitted on : Wednesday, September 8, 2010 - 11:29:37 AM
Last modification on : Saturday, September 19, 2020 - 4:34:50 AM

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

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