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Exploratory Analysis of Functional Data via Clustering and Optimal Segmentation

dc.contributor.authorRossi, Fabrice
HAL ID: 77
dc.contributor.authorLechevallier, Yves
dc.contributor.authorHugueney, Bernard
dc.contributor.authorHébrail, Georges
dc.date.accessioned2011-04-20T13:52:50Z
dc.date.available2011-04-20T13:52:50Z
dc.date.issued2010
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6023
dc.language.isoenen
dc.subjectDynamic programmingen
dc.subjectSegmentationen
dc.subjectClusteringen
dc.subjectExploratory analysisen
dc.subjectMultiple time seriesen
dc.subjectFunctional Dataen
dc.subject.ddc519
dc.titleExploratory Analysis of Functional Data via Clustering and Optimal Segmentationen
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenWe 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.en
dc.relation.isversionofjnlnameNeurocomputing
dc.relation.isversionofjnlvol73en
dc.relation.isversionofjnlissue7-9en
dc.relation.isversionofjnldate2010-03
dc.relation.isversionofjnlpages1125-1141en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.neucom.2009.11.022en
dc.identifier.urlsitehttp://fr.arxiv.org/abs/1004.0456en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelProbabilités et mathématiques appliquées


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