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hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierQuality control and dynamic reliability [CQFD]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierUsage-centered design, analysis and improvement of information systems [AxIS]
dc.contributor.authorLECHEVALLIER, Yves
hal.structure.identifierOptimisation Combinatoire [G-SCOP_OC]
dc.contributor.authorBRIANT, Olivier
dc.date.issued2007
dc.identifier.issn0167-9473
dc.description.abstractEnDIVCLUS-T is a divisive hierarchical clustering algorithm based on a monothetic bipartitional approach allowing the dendrogram of the hierarchy to be read as a decision tree. It is designed for either numerical or categorical data. Like the Ward agglomerative hierarchical clustering algorithm and the k-means partitioning algorithm, it is based on the minimization of the inertia criterion. However, unlikeWard and k-means, it provides a simple and natural interpretation of the clusters. The price paid by construction in terms of inertia by DIVCLUS-T for this additional interpretation is studied by applying the three algorithms on six databases from the UCI Machine Learning repository.
dc.language.isoen
dc.publisherElsevier
dc.subject.enDivisive clustering
dc.subject.enMonothetic cluster
dc.subject.enDecision dendrogram
dc.subject.enInertia criterion
dc.title.enDIVCLUS-T: a monothetic divisive hierarchical clustering method
dc.typeArticle de revue
dc.identifier.doi10.1016/j.csda.2007.03.013
dc.subject.halStatistiques [stat]/Autres [stat.ML]
dc.subject.halInformatique [cs]/Apprentissage [cs.LG]
bordeaux.journalComputational Statistics and Data Analysis
bordeaux.page687-701
bordeaux.volume52
bordeaux.issue2
bordeaux.peerReviewedoui
hal.identifierhal-00260963
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00260963v1
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