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dc.contributor.authorDe A. T. De Carvalho, Francisco
dc.contributor.authorCsernel, Marc
dc.contributor.authorLechevallier, Yves
dc.date.accessioned2014-01-13T12:59:30Z
dc.date.available2014-01-13T12:59:30Z
dc.date.issued2009-08
dc.identifier.urihttps://basepub.dauphine.fr/handle/123456789/12414
dc.language.isoenen
dc.subjectSymbolic Data Analysisen
dc.subjectClustering algorithmsen
dc.subjectNormal symbolic formen
dc.subjectConstraintsen
dc.subjectDissimilarity functionsen
dc.subject.ddc519en
dc.subject.classificationjelC02en
dc.subject.classificationjelC89en
dc.titleClustering constrained symbolic dataen
dc.typeArticle accepté pour publication ou publié
dc.contributor.editoruniversityotherINRIA;France
dc.contributor.editoruniversityotherUniversidade Federal de Pernambuco;Brésil
dc.description.abstractenDealing with multi-valued data has become quite common in both the framework of databases as well as data analysis. Such data can be constrained by domain knowledge provided by relations between the variables and these relations are expressed by rules. However, such knowledge can introduce a combinatorial increase in the computation time depending on the number of rules. In this paper, we present a way to cluster such data in polynomial time. The method is based on the following: a decomposition of the data according to the rules, a suitable dissimilarity function and a clustering algorithm based on dissimilarities.en
dc.relation.isversionofjnlnamePattern Recognition Letters
dc.relation.isversionofjnlvol30en
dc.relation.isversionofjnlissue11en
dc.relation.isversionofjnldate2009-08
dc.relation.isversionofjnlpages1037–1045en
dc.relation.isversionofdoihttp://dx.doi.org/10.1016/j.patrec.2009.04.009en
dc.relation.isversionofjnlpublisherElsevieren
dc.subject.ddclabelProbabilités et mathématiques appliquéesen
dc.relation.forthcomingnonen
dc.relation.forthcomingprintnonen


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