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

TL;DR

This paper introduces an information-theoretic clustering method for relational matrices that adaptively determines the number of clusters without prior specification, using a deterministic annealing approach.

Contribution

It presents a novel clustering technique based on the value of information, enabling data-driven determination of cluster number and improved partitioning of similarity or dissimilarity matrices.

Findings

Automatically determines the number of clusters during annealing.

Identifies the global-best partition effectively.

Avoids the need for pre-specifying cluster count.

Abstract

In this paper, we provide an approach to clustering relational matrices whose entries 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 associated with changes in information. Optimizing the value of information yields a deterministic annealing style of clustering with many benefits. For instance, investigators avoid needing to a priori specify the number of clusters, as the partitions naturally undergo phase changes, during the annealing process, whereby the number of clusters changes in a data-driven fashion. The global-best partition can also often be identified.