ClustOfVar: AnRPackage for the Clustering of Variables

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.

Automated vehicles: Multivariate analysis of drivers' take-over behaviour

“…1) ClustofVar: ClustOfVar [9] is an algorithm for the statistical software R [10]. It detects groups of variables by using a homogeneity criterion that is based on the sum of correlation ratios for qualitative variables, and squared correlations for quantitative variables.…”

Representing variables in the latent space

“…, CK )로 표기하자. R 팩키지 ClustOfVar과 ClustVarLV에 잠재적 변수군집화 방법이 구현되어 있다 (Chavent et al, 2012(Chavent et al, , 2013.…”

Assessment of Participation in Cultural Activities in Poland by Selected Multivariate Methods

“…The second cluster includes the activities carried out outside home and probably requiring more time and financial expenditures. The homogeneity of the clusters can be evaluated by the degree of association between the variables constituting the cluster and the central synthetic variable; in the case of qualitative variables correlation ratio is applied for this purpose (Chavent et al 2013). The results in Table 1 show that the partition into three clusters is reasonable as the correlation ratios are relatively high.…”