Joint Dimension Reduction and Clustering

Cluster analysis is a technique that attempts to divide objects into similar groups. As described in previous studies, cluster analysis works poorly when variables that do not reflect the clustering structure are present in the dataset or when the number of variables is large. In order to tackle this problem, several methods have been proposed that jointly perform clustering of objects and dimension reduction of the variables. In this chapter, we review the technique whereby multiple correspondence analysis and k-means clustering are combined in order to investigate the relationships between qualitative variables.