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Clustering alternatives in preference-approvals via novel pseudometrics.
- Source :
- Statistical Methods & Applications; Mar2024, Vol. 33 Issue 1, p61-87, 27p
- Publication Year :
- 2024
-
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. [ABSTRACT FROM AUTHOR]
- Subjects :
- MULTIDIMENSIONAL scaling
PARALLEL algorithms
BASE pairs
VOTING
Subjects
Details
- Language :
- English
- ISSN :
- 16182510
- Volume :
- 33
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- Statistical Methods & Applications
- Publication Type :
- Academic Journal
- Accession number :
- 177775692
- Full Text :
- https://doi.org/10.1007/s10260-023-00718-w