Please use this identifier to cite or link to this item: http://arl.liuc.it/dspace/handle/2468/6478
Title: Clustering ranked data using copulas
Authors: Nai Ruscone, Marta
Issue Date: 2019
Publisher: Artion conferences & events
Bibliographic citation: Nai Ruscone Marta (2019), Clustering ranked data using copulas. In: 16th conference of the International federation of classification societies: 26-29 August 2019, Thessaloniki Concert Hall, Thessaloniki, Greece, #IFCS2019: abstract book. Kalamaria: Artion conferences & events, p. 127.
Abstract: Clustering of ranking data aims at the identification of groups of subjects with a homogenous, common, preference behavior. Human beings naturally tend to rank objects in the everyday life such as shops, one’s place of living, choice of occupations, singers and football teams, according to their preferences. More generally, ranking data occurs when a number of subjects are asked to rank a list of objects according to their personal preference order. The input in cluster analysis is a dissimilarity matrix quantifying the differences between rankings of two subjects. The choice of the dissimilarity dramatically affects the classification outcome and therefore the computation of an appropriate dissimilarity matrix is an issue. Several distance measures have been proposed for ranking data. We propose generalizations of this kind of distance using copulas adapted to the case of missing data. We consider the case of the extreme list where only the top-k and/or bottom-k ranks are known. We discuss an optimistic and a pessimistic imputation of missing values and show its effect on the classification. Those generalizations provide a more flexible instrument to model different types of data dependence structures and consider different situations in the classification process. Simulated and real data are used to illustrate the performance and the importance of our proposal.
URI: http://arl.liuc.it/dspace/handle/2468/6478
Journal/Book: 16th conference of the International federation of classification societies: 26-29 August 2019, Thessaloniki Concert Hall, Thessaloniki, Greece, #IFCS2019: abstract book
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