Clustering of Variables for Mixed Data
SARACCO, Jerome
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Ecole Nationale Supérieure de Cognitique [ENSC]
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]
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
SARACCO, Jerome
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Ecole Nationale Supérieure de Cognitique [ENSC]
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]
< Reduce
Quality control and dynamic reliability [CQFD]
Institut de Mathématiques de Bordeaux [IMB]
Language
en
Chapitre d'ouvrage
This item was published in
Statistics for Astrophysics: Clustering and Classification, Statistics for Astrophysics: Clustering and Classification. 2016, vol. 77, p. 91-119
EDP Sciences
English Abstract
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 ...Read more >
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).Read less <
Origin
Hal imported