Selection of Variables for Cluster Analysis and Classification Rules

Abstract

In this article we introduce two procedures for variable selection in cluster analysis and classification rules. One is mainly aimed at detecting the “noisy” noninformative variables, while the other also deals with multicolinearity and general dependence. Both methods are designed to be used after a “satisfactory” grouping procedure has been carried out. A forward–backward algorithm is proposed to make such procedures feasible in large datasets. A small simulation is performed and some real data examples are analyzed.