A HIERARCHICAL CLUSTERING METHOD FOR RANK ORDER DATA

Abstract

For a data matrix composed of subjects by rank orders, a hierarchical clustering method is presented, which partitions subjects into statistically homogeneous clusters on the basis of Kendall's coefficient of concordance W. The algorithm has been found to work successfully. Illustrative examples of the clustering of real data are offered. The method is useful for both preliminary analysis and confirmatory analysis of nonmetric multidimensional scaling of rank order data, especially in large samples. This method is also compared with other hierarchical clustering methods.