2026-04-14T16:19:47Zhttps://uvadoc.uva.es/oai/request

oai:uvadoc.uva.es:10324/723652024-12-11T20:04:19Zcom_10324_1146com_10324_931com_10324_894col_10324_1262

Albano, Alessandro García Lapresta, José Luis Plaia, Antonella Sciandra, Mariangela 2024-12-11T10:04:08Z 2024-12-11T10:04:08Z 2023 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 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. eng info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/4.0/ © 2023, The Author(s) Atribución 4.0 Internacional Clustering alternatives in preference-approvals via novel pseudometrics info:eu-repo/semantics/article