ClustOfVar: an R package for the clustering of variables

Marie Chavent*, Vanessa Kuentz-Simonet, Benoît Liquet, Jérôme Saracco

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

96 Citations (Scopus)

Abstract

Clustering of variables is as a way to arrange variables into homogeneous clusters, i.e., groups of variables which are strongly related to each other and thus bring the same information. These approaches can then be useful for dimension reduction and variable selection. Several specific methods have been developed for the clustering of numerical variables. However concerning qualitative variables or mixtures of quantitative and qualitative variables, far fewer methods have been proposed. The R package ClustOfVar was specifically developed for this purpose. The homogeneity criterion of a cluster is defined as the sum of correlation ratios (for qualitative variables) and squared correlations (for quantitative variables) to a synthetic quantitative variable, summarizing “as good as possible” the variables in the cluster. This synthetic variable is the first principal component obtained with the PCAMIX method. Two clustering algorithms are proposed to optimize the homogeneity criterion: iterative relocation algorithm and ascendant hierarchical clustering. We also propose a bootstrap approach in order to determine suitable numbers of clusters. We illustrate the methodologies and the associated package on small datasets.

Original languageEnglish
Pages (from-to)1-16
Number of pages16
JournalJournal of Statistical Software
Volume50
Issue number13
DOIs
Publication statusPublished - Sep 2012
Externally publishedYes

Bibliographical note

Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • dimension reduction
  • hierarchical clustering of variables
  • k-means clustering of variables
  • mixture of quantitative and qualitative variables
  • stability

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