Identification of noisy variables for nonmetric and symbolic data in cluster analysis

Abstract

A proposal of an extended version of the HINoV method for the iden- tification of the noisy variables (Carmone et al [1999]) for nonmetric, mixed, and symbolic interval data is presented in this paper. Proposed modifications are eval- uated on simulated data from a variety of models. The models contain the known structure of clusters. In addition, the models contain a different number of noisy (irrelevant) variables added to obscure the underlying structure to be recovered.