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.

翻译：本文研究群体决策中的聚类问题，其中专家意见以成对比较矩阵形式给出。特别地，我们建议采用k-medoids模型对矩阵进行分类，该模型具有线性规划问题表述形式，能够包含对聚类中心性质的任意约束条件。其目标函数取决于矩阵间相异度的度量方式，而不依赖于从矩阵导出的权重向量。本方法为决策支持提供了便捷工具，例如可用于量化群体意见聚合的可信度。所提出的理论框架在一个大规模实验数据集上得到应用，该框架能够自动检测决策者产生的某些错误，并识别不一致性的共同来源。