  In this paper we introduce two procedures for variable selection in cluster
analysis and classification rules. One is mainly oriented to detect the noisy
non-informative variables, while the other deals also with multicolinearity. A
forward-backward algorithm is also proposed to make feasible these procedures
in large data sets. A small simulation is performed and some real data examples
are analyzed.
