Uncertainty Sampling for Constrained Cluster Ensemble
Masayuki Okabe, Seiji Yamada · 2013
Constrained Clustering is a framework of improving clustering performance by using a set of constraints about data pairs. Since performance of constrained clustering depends on a set of constraints to use, we need a method to select good constraints that are expected to promote clustering performance. In this paper, we propose such a method, which actively selects data pairs to be constrained by using variance of clustering iteration. This method consists of a boosting based cluster ensemble algorithm that integrates a set of clusters produced by a constrained k-means with controlled data assignment order. Experimental results show that our method outperforms clustering with random sampling method.