2026-04-28T19:29:49Zhttps://uvadoc.uva.es/oai/requestoai:uvadoc.uva.es:10324/723652024-12-11T20:04:19Zcom_10324_1146com_10324_931com_10324_894col_10324_1262
Clustering alternatives in preference-approvals via novel pseudometrics
Albano, Alessandro
García Lapresta, José Luis
Plaia, Antonella
Sciandra, Mariangela
Producción Científica
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.
2024-12-11
2024-12-11
2023
info:eu-repo/semantics/article
Statistical Methods & Applications, 2024, vol. 33, n. 1, pp. 61-87
1618-2510
https://uvadoc.uva.es/handle/10324/72365
10.1007/s10260-023-00718-w
61
1
87
Statistical Methods & Applications
33
1613-981X
eng
https://link.springer.com/article/10.1007/s10260-023-00718-w
info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/4.0/
© 2023, The Author(s)
Atribución 4.0 Internacional
Springer