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Clustering of Variables for Mixed Data

hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
hal.structure.identifierEcole Nationale Supérieure de Cognitique [ENSC]
dc.contributor.authorSARACCO, Jerome
hal.structure.identifierQuality control and dynamic reliability [CQFD]
hal.structure.identifierInstitut de Mathématiques de Bordeaux [IMB]
dc.contributor.authorCHAVENT, Marie
dc.date.accessioned2024-04-04T03:12:13Z
dc.date.available2024-04-04T03:12:13Z
dc.date.issued2016
dc.identifier.isbn978-2-7598-9001-9
dc.identifier.urihttps://oskar-bordeaux.fr/handle/20.500.12278/193847
dc.description.abstractEnThis chapter presents clustering of variables which aim is to lump together strongly related variables. The proposed approach works on a mixed data set, i.e. on a data set which contains numerical variables and categorical variables. Two algorithms of clustering of variables are described: a hierarchical clustering and a k-means type clustering. A brief description of PCAmix method (that is a principal component analysis for mixed data) is provided, since the calculus of the synthetic variables summarizing the obtained clusters of variables is based on this multivariate method. Finally, the R packages {\bf ClustOfVar} and {\bf PCAmixdata} are illustrated on real mixed data. The PCAmix (resp. ClustOfVar) approach is first used for dimension reduction (step1) before standard clustering of the individuals (step 2).
dc.language.isoen
dc.publisherEDP Sciences
dc.source.titleStatistics for Astrophysics: Clustering and Classification
dc.title.enClustering of Variables for Mixed Data
dc.typeChapitre d'ouvrage
dc.subject.halStatistiques [stat]
bordeaux.page91-119
bordeaux.volume77
bordeaux.hal.laboratoriesInstitut de Mathématiques de Bordeaux (IMB) - UMR 5251*
bordeaux.institutionUniversité de Bordeaux
bordeaux.institutionBordeaux INP
bordeaux.institutionCNRS
bordeaux.title.proceedingStatistics for Astrophysics: Clustering and Classification
hal.identifierhal-01417442
hal.version1
hal.popularnon
hal.audienceInternationale
hal.origin.linkhttps://hal.archives-ouvertes.fr//hal-01417442v1
bordeaux.COinSctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.btitle=Statistics%20for%20Astrophysics:%20Clustering%20and%20Classification&rft.date=2016&rft.volume=77&rft.spage=91-119&rft.epage=91-119&rft.au=SARACCO,%20Jerome&CHAVENT,%20Marie&rft.isbn=978-2-7598-9001-9&rft.genre=unknown


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