Clustering with Constrained Similarity Learning - Okabe, Masayuki; Yamada, Seiji (Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 03, 2009)
This paper proposes a method of learning a similarity matrix from pairwise constraints for interactive clustering. The similarity matrix can be learned by solving an optimization problem as semi-definite programming where we give additional constraints about neighbors of constrained pairwise data besides original constraints. For interactive clustering, since we can get only a few pairwise constraints from a user, we need to extend such constraints to richer ones. Thus this proposed method to extend the pairwise constraints to space-level ones is effective to interactive clustering. First we formalize clustering with constrained similarity learning, and then introduce the extended constraints as linear constraints. We verify the effectiveness of our proposed method by applying it on a simple clustering task. The results of the experiments shows that our method is promising.