A clustering approach for pairwise comparison matrices
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Acceso en línea: | |
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| _version_ | 1866915244272517120 |
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| author | Ágoston, Kolos Csaba Bozóki, Sándor Csató, László |
| author_facet | Ágoston, Kolos Csaba Bozóki, Sándor Csató, László |
| contents | We consider clustering in group decision making where the opinions are given by pairwise comparison matrices. In particular, the k-medoids model is suggested to classify the matrices since it has a linear programming problem formulation that may contain any condition on the properties of the cluster centres. Its objective function depends on the measure of dissimilarity between the matrices but not on the weights derived from them. Our methodology provides a convenient tool for decision support, for instance, it can be used to quantify the reliability of the aggregation. The proposed theoretical framework is applied to a large-scale experimental dataset, on which it is able to automatically detect some mistakes made by the decision-makers, as well as to identify a common source of inconsistency. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_06061 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A clustering approach for pairwise comparison matrices Ágoston, Kolos Csaba Bozóki, Sándor Csató, László Optimization and Control Applications 90-10, 90B50, 91B08 We consider clustering in group decision making where the opinions are given by pairwise comparison matrices. In particular, the k-medoids model is suggested to classify the matrices since it has a linear programming problem formulation that may contain any condition on the properties of the cluster centres. Its objective function depends on the measure of dissimilarity between the matrices but not on the weights derived from them. Our methodology provides a convenient tool for decision support, for instance, it can be used to quantify the reliability of the aggregation. The proposed theoretical framework is applied to a large-scale experimental dataset, on which it is able to automatically detect some mistakes made by the decision-makers, as well as to identify a common source of inconsistency. |
| title | A clustering approach for pairwise comparison matrices |
| topic | Optimization and Control Applications 90-10, 90B50, 91B08 |
| url | https://arxiv.org/abs/2402.06061 |