  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
algorithms for the clustering of variables are proposed: 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.
