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hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorCHAVENT, Marie
hal.structure.identifierUsage-centered design, analysis and improvement of information systems [AxIS]
dc.contributor.authorLECHEVALLIER, Yves
dc.contributor.editorV. Batagelj et al.
dc.date.accessioned2024-04-04T02:53:37Z
dc.date.available2024-04-04T02:53:37Z
dc.date.issued2006-09
dc.date.conference2006-07
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/192192
dc.description.abstractEnDIVCLUS-T is a descendant hierarchical clustering methods based on the same monothetic approach than segmentation but from an unsupervised point of view. The dendrogram of the hierarchy is easy to interpret and can be read as decision tree. We present DIVCLUS-T on a small numerical and a small categorical example. DIVCLUS-T is then compared with two polythetic clustering methods: the Ward ascendant hierarchical clustering method and the k-means partitional method. The three algoritms are applied and compared on six databases of the UCI Machine Learning repository.
dc.language.isoen
dc.publisherSpringer
dc.source.titleData Science and Classification
dc.title.enEmpirical comparison of a monothetic divisive clustering method with the Ward and the k-means clustering methods
dc.typeCommunication dans un congrès
dc.identifier.doi10.1007/3-540-34416-0
dc.subject.halStatistiques [stat]/Autres [stat.ML]
bordeaux.page83-90
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.conference.titleIFCS 2006
bordeaux.countrySI
bordeaux.title.proceedingData Science and Classification
bordeaux.conference.cityLjubljana
bordeaux.peerReviewedoui
hal.identifierhal-00278292
hal.version1
hal.invitednon
hal.proceedingsoui
hal.popularnon
hal.audienceNon spécifiée
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-00278292v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Data%20Science%20and%20Classification&rft.date=2006-09&rft.spage=83-90&rft.epage=83-90&rft.au=CHAVENT,%20Marie&LECHEVALLIER,%20Yves&rft.genre=unknown


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