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 preferenceapproval 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.

Albano, A., García-Lapresta, J.L., Plaia, A., Sciandra, M. (2023). Clustering alternatives in preference-approvals via novel pseudometrics. STATISTICAL METHODS & APPLICATIONS [10.1007/s10260-023-00718-w].

Clustering alternatives in preference-approvals via novel pseudometrics

Albano, Alessandro
;
Plaia, Antonella;Sciandra, Mariangela
2023-01-01

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 preferenceapproval 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.
2023
Settore SECS-S/01 - Statistica
Albano, A., García-Lapresta, J.L., Plaia, A., Sciandra, M. (2023). Clustering alternatives in preference-approvals via novel pseudometrics. STATISTICAL METHODS & APPLICATIONS [10.1007/s10260-023-00718-w].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10447/606353
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