  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.
