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In this paper, we provide an approach to clustering relational matrices whoseentries correspond to either similarities or dissimilarities between objects.Our approach is based on the value of information, a parameterized,information-theoretic criterion that measures the change in costs associatedwith changes in information. Optimizing the value of information yields adeterministic annealing style of clustering with many benefits. For instance,investigators avoid needing to a priori specify the number of clusters, as thepartitions naturally undergo phase changes, during the annealing process,whereby the number of clusters changes in a data-driven fashion. Theglobal-best partition can also often be identified.

Partitioning Relational Matrices of Similarities or Dissimilarities using the Value of Information