Selection of Variables for Cluster Analysis and Classification Rules
Ricardo Fraiman, Ana Benito Justel, Marcela Svarc · Journal of the American Statistical Association · 2008
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.