Optimal clustering by merge-based branch-and-bound

Abstract

We present a method to construct optimal clustering via a sequence of merge steps. We formulate the merge-based clustering as a minimum redundancy search tree, and then search the optimal clustering by a branch-and-bound technique. Optimal clustering is found regardless of the objective function used. We also consider two suboptimal polynomial time variants based on the proposed branch-and-bound technique. However, all variants are slow and has merely theoretical interest. We discuss the reasons for the results.