RT info:eu-repo/semantics/article
T1 Clustering alternatives in preference-approvals via novel pseudometrics
A1 Albano, Alessandro
A1 García Lapresta, José Luis
A1 Plaia, Antonella
A1 Sciandra, Mariangela
K1 Preference-approvals
K1 Pseudometric
K1 Clustering
K1 Non metric multidimensional scaling
K1 Voting systems
AB 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.
PB Springer
SN 1618-2510
YR 2023
FD 2023
LK https://uvadoc.uva.es/handle/10324/72365
UL https://uvadoc.uva.es/handle/10324/72365
LA eng
NO Statistical Methods & Applications, 2024, vol. 33, n. 1, pp. 61-87
NO Producción Científica
DS UVaDOC
RD 22-dic-2024