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Clustering alternatives in preference-approvals via novel pseudometrics

Author

Listed:
  • Alessandro Albano

    (University of Palermo)

  • José Luis García-Lapresta

    (Universidad de Valladolid)

  • Antonella Plaia

    (University of Palermo)

  • Mariangela Sciandra

    (University of Palermo)

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.

Suggested Citation

  • Alessandro Albano & José Luis García-Lapresta & Antonella Plaia & Mariangela Sciandra, 2024. "Clustering alternatives in preference-approvals via novel pseudometrics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 33(1), pages 61-87, March.
    • Handle: RePEc:spr:stmapp:v:33:y:2024:i:1:d:10.1007_s10260-023-00718-w
    DOI: 10.1007/s10260-023-00718-w
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