Dissimilarity measure for ranking data via mixture of copulae

Andrea Bonanomi*, Marta Nai Ruscone, Silvia Angela Osmetti

*Autore corrispondente per questo lavoro

Risultato della ricerca: Contributo in rivistaArticolo in rivistapeer review

2 Citazioni (Scopus)


We define a new distance measure for ranking data using a mixture of copula functions. Our distance measure evaluates the dissimilarity of subjects’ ranking preferences to segment them via hierarchical cluster analysis. The proposed distance measure builds upon Spearman grade correlation coefficient on a copula transformation of rank denoting the level of importance assigned by subjects on the classification of k objects. These mixtures of copulae enable flexible modeling of the different types of dependence structures found in data and the consideration of various circumstances in the classification process. For example, by using mixtures of copulae with lower and upper tail dependence, we can emphasize the agreement on extreme ranks when they are considered important.
Lingua originaleEnglish
pagine (da-a)412-425
Numero di pagine14
RivistaStatistical Analysis and Data Mining
Stato di pubblicazionePubblicato - 2019


  • distance measure
  • hierarchical cluster analysis
  • mixture of copulae
  • ranking data


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