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Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

Rossi, Fabrice; Lechevallier, Yves; Hugueney, Bernard; Hébrail, Georges (2010), Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation, Neurocomputing, 73, 7-9, p. 1125-1141. http://dx.doi.org/10.1016/j.neucom.2009.11.022

Type
Article accepté pour publication ou publié
Lien vers un document non conservé dans cette base
http://fr.arxiv.org/abs/1004.0456
Date
2010
Nom de la revue
Neurocomputing
Volume
73
Numéro
7-9
Éditeur
Elsevier
Pages
1125-1141
Identifiant publication
Métadonnées
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Auteur(s)
Rossi, Fabrice
Lechevallier, Yves
Hugueney, Bernard
Hébrail, Georges
Résumé (EN)
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.
Mots-clés
Dynamic programming; Segmentation; Clustering; Exploratory analysis; Multiple time series; Functional Data