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.

翻译：本文研究群体决策中的聚类问题，其中决策意见以成对比较矩阵形式给出。特别地，我们提出采用k-medoids模型对矩阵进行分类，因其具有线性规划问题形式。该模型的目标函数取决于矩阵间的相异度度量，而非从矩阵中导出的权重。当仅包含单个聚类时，本方法可作为传统聚合流程的替代方案，同时可用于量化聚合结果的可靠性。将该理论框架应用于大规模实验数据集时，能够自动检测决策者所犯的部分错误。