Defining subjects distance in hierarchical cluster analysis by copula approach

• Published in: Quality & quantity Vol. 51; no. 2; pp. 859 - 872
• Main Authors: Bonanomi, Andrea, Nai Ruscone, Marta, Osmetti, Silvia Angela
• Format: Journal Article
• Language: English
• Published: Dordrecht Springer Netherlands 01.03.2017; Springer; Springer Nature B.V
• Subjects: Agreements; Algorithms; Classification; Cluster analysis; Clustering; Consumers; Data analysis; Methodology of the Social Sciences; Preferences; Random variables; Ratings & rankings; Social Sciences; Studies; New York; United States > US; Italy; Copula function; Hierarchical cluster analysis; Ordinal data

We propose a new measure to evaluate the distance between subjects expressing their preferences by rankings in order to segment them by hierarchical cluster analysis. The proposed index builds upon the Spearman’s grade correlation coefficient on a transformation, operated by the copula function, of...