Aggregation of pairwise comparison matrices: A clustering approach
arxiv(2024)
摘要
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 as it has a linear programming problem
formulation. Its objective function depends on the measure of dissimilarity
between the matrices but not on the weights derived from them. With one
cluster, our methodology provides an alternative to the conventional
aggregation procedures. It can also 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.
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