Preliminary Investigation on Constraint Selection for Constrained Clustering using Subordinate Cluster Generation

Abstract

This paper reports the effect of constraint selection on constrained clustering using subordinate cluster generation based on Bayesian Information Criterion (BIC). Constrained clustering has the difficulty in handling pair-wise constraints located far away from each other. To address this difficulty, constrained clustering using subordinate cluster generation is proposed. This method aims to handle the difficulty using subordinate clusters that are temporally generated and finally merged into their master cluster during clustering process. However, there is a case that small clusters are generated by subordinate cluster generation, which can have a negative influence to a final clustering result. To investigate the influence by constraints, this paper reports the effect of constraint selection on a final clustering result using synthesis datasets.