The aim of clustering variables is to divide a set of numeric variables into disjoint clusters (subset of variables). In these slides, we present an approach based on the concept of latent component. A subset of variables is summarized by a latent component which is the first factor from the principal component analysis. This is a kind of "centroid" variable which maximizes the sum of the squared correlation with the existing variables. Various clustering algorithms based on this idea are described: a hierarchical agglomerative algorithm; a top down approach; and an approach which is inspired by the k-means method.
Keywords: clustering, clustering variables, latent variable, latent component, clusters, groups, bottom-up, hierarchical agglomerative clustering, top down, varclus, k-means, pca, principal component analysis
Components (Tanagra): VARHCA, VARKMEANS, VARCLUS
Slides: Clustering variables
Tutorials:
Tanagra tutorials, "Variable clustering (VARCLUS)", 2008.
