Dissimilarity measure for ranking data via copula

A new distance measure is defined for ranking data by using copula functions. This distance evaluates the dissimilarity between subjects expressing their preferences by rankings in order to segment them by hierarchical cluster analysis. The proposed distance builds upon the Spearmans grade correlation coefficient on a transformation of the ranks denoting the levels of the importance assigned by subjects under classification to k objects. The copula is a flexible way to model different types of dependence structures in the data and to consider different situations in the classification process. For example, by using copulae with lower and upper tail dependence, we emphasize the agreement on extreme ranks, when they are considered more important.