Clustering alternatives in preference-approvals via novel pseudometrics

Alessandro Albano (), José Luis García-Lapresta, Antonella Plaia () and Mariangela Sciandra ()
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Alessandro Albano: University of Palermo
Antonella Plaia: University of Palermo
Mariangela Sciandra: University of Palermo

Statistical Methods & Applications, 2024, vol. 33, issue 1, No 3, 87 pages

Abstract: Abstract Preference-approval structures combine preference rankings and approval voting for declaring opinions over a set of alternatives. In this paper, we propose a new procedure for clustering alternatives in order to reduce the complexity of the preference-approval space and provide a more accessible interpretation of data. To that end, we present a new family of pseudometrics on the set of alternatives that take into account voters’ preferences via preference-approvals. To obtain clusters, we use the Ranked k-medoids (RKM) partitioning algorithm, which takes as input the similarities between pairs of alternatives based on the proposed pseudometrics. Finally, using non-metric multidimensional scaling, clusters are represented in 2-dimensional space.

Keywords: Preference-approvals; Pseudometric; Clustering; Non metric multidimensional scaling; Voting systems (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10260-023-00718-w

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