Graph-based Clustering with Background Knowledge

Since 2000, when clustering with side information is introduced in the first time, so many semi-supervised clustering algorithms have been presented. Semi-supervised clustering, that integrates side information (seeds or constraints) in the clustering process, has been known as a good strategy to boost clustering results. In general, semi-supervised clustering focuses on two kind of side information including seeds and constraints, not much attention was given to the topic of using both seeds and constraints in the same algorithm. To address this problem, in this paper, we extend the semi-supervised graph based clustering (SSGC) by embedding both constraints and seeds in the clustering process; our new algorithm is called MCSSGC. Experiments conducted on real data sets from UCI show that our method can produce good clustering results compared with SSGC.