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Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

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Date
2010
Link to item file
http://fr.arxiv.org/abs/1004.0456
Dewey
Probabilités et mathématiques appliquées
Sujet
Dynamic programming; Segmentation; Clustering; Exploratory analysis; Multiple time series; Functional Data
Journal issue
Neurocomputing
Volume
73
Number
7-9
Publication date
03-2010
Article pages
1125-1141
Publisher
Elsevier
DOI
http://dx.doi.org/10.1016/j.neucom.2009.11.022
URI
https://basepub.dauphine.fr/handle/123456789/6023
Collections
  • LAMSADE : Publications
Metadata
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Author
Rossi, Fabrice
Lechevallier, Yves
Hugueney, Bernard
Hébrail, Georges
Type
Article accepté pour publication ou publié
Abstract (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.

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