SARACCO, Jerome; CHAVENT, Marie

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SARACCO, Jerome
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Ecole Nationale Supérieure de Cognitique [ENSC]

CHAVENT, Marie
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]

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Statistics for Astrophysics: Clustering and Classification, Statistics for Astrophysics: Clustering and Classification. 2016, vol. 77, p. 91-119

EDP Sciences

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This 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).< Réduire

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Clustering of Variables for Mixed Data

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