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Title: Defining subjects distance in hierarchical cluster analysis by copula approach
Authors: Bonanomi, Andrea
Nai Ruscone, Marta
Osmetti, Silvia Angela
Issue Date: 2017
Publisher: Springer
Bibliographic citation: Bonanomi Andrea, Nai Ruscone Marta, Osmetti Silvia Angela (2017), Defining subjects distance in hierarchical cluster analysis by copula approach. In: Quality & quantity, vol. 51, n. 2, 1 March 2017, p. 859-872. Published electronically 19 October 2016. ISSN 0033-5177. E-ISSN 1573-7845. DOI 10.1007/s11135-016-0444-9.
Abstract: 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 the position/rank denoting the level of the importance assigned by subjects under classification to k objects. In particular, by using the copula functions with tail dependence we obtain an index suitable for emphasizing the agreement on top ranks, when the top ranks are considered more important than the lower ones. We evaluate the performance of our proposal by an example on simulated data, showing that the resulting groups contain subjects whose preferences are more similar on the most important ranks. A further application with real data confirms the pertinence and the importance of our proposal.
Journal/Book: Quality and quantity
ISSN: 0033-5177
Appears in Collections:Contributo in rivista

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