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dc.contributor.authorVrac, Mathieu
dc.contributor.authorDiday, Edwin
dc.date.accessioned2011-06-28T13:11:33Z
dc.date.available2011-06-28T13:11:33Z
dc.date.issued2005
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/6620
dc.language.isoenen
dc.subjectClusteringen
dc.subjectCopulasen
dc.subjectData miningen
dc.subjectMixture decompositionen
dc.subjectPartitioningen
dc.subjectSymbolic data analysisen
dc.subject.ddc519en
dc.titleMixture decomposition of distributions by copulas in the symbolic data analysis frameworken
dc.typeArticle accepté pour publication ou publié
dc.description.abstractenThis work investigates the situation in which each unit from a given set is described by some vector of p probability distributions. Our aim is to find simultaneously a “good” partition of these units and a probabilistic description of the clusters with a model using “copula functions” associated with each class of this partition. Different copula models are presented. The mixture decomposition problem is resolved in this general case. This result extends the standard mixture decomposition problem to the case where each unit is described by a vector of distributions instead of the traditional classical case where each unit is described by a vector of single (categorical or numerical) values. Several generalizations of some standard algorithms are proposed. All these results are first considered in the case of a single variable and then extended to the case of a vector of p variables by using a top-down binary tree approach.en
dc.relation.isversionofjnlnameDiscrete Applied Mathematics
dc.relation.isversionofjnlvol147en
dc.relation.isversionofjnlissue1en
dc.relation.isversionofjnldate2005
dc.relation.isversionofjnlpages27-41en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.dam.2004.06.018en
dc.description.sponsorshipprivateouien
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen


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