Comparing Approaches for Clustering Mixed Mode Data: An Application in Marketing Research

Abstract

Practical applications in marketing research often involve mixtures of categorical and continuous variables. For the purpose of clustering, a variety of algorithms has been proposed to deal with mixed mode data. In this paper we apply some of these techniques on two data sets regarding marketing problems. We also propose an approach based on the consensus between partitions obtained by considering separately each variable or subsets of variables having the same scale. This approach may be applied to data with many categorical variables and does not impose restrictive assumptions on the variable distribution. We finally suggest a summarizing fuzzy partition with membership degrees obtained as a function of the classes determined by the different methods.