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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.
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Marie Chavent
Centre National de la Recherche Scientifique
Vanessa Kuentz-Simonet
Centre d'Études Scientifiques et Techniques d'Aquitaine
Benoît Liquet
Centre National de la Recherche Scientifique
Journal of Statistical Software
Université de Bordeaux
Institut de Mathématiques de Bordeaux
Laboratoire Dynamiques Sociales et Recomposition des Espaces
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Chavent et al. (Sun,) studied this question.
synapsesocial.com/papers/6a197a490b4377da65580776 — DOI: https://doi.org/10.18637/jss.v050.i13