Weighting variables in K-means clustering

Abstract

The aim of this study is to assign weights w 1,..., wm to m clustering variables Z 1,..., Z m, so that k groups were uncovered to reveal more meaningful within-group coherence. We propose a new criterion to be minimized, which is the sum of the weighted within-cluster sums of squares and the penalty for the heterogeneity in variable weights w 1,..., w m. We will present the computing algorithm for such k-means clustering, a working procedure to determine a suitable value of penalty constant and numerical examples, among which one is simulated and the other two are real.