Semi-Supervised Clustering via Matrix Factorization

Abstract The recent years have witnessed a surge of interests of semi-supervised clustering methods, which aim to clus- ter the data set under the guidance of some supervi- sory information. Usually those supervisory informa- tion takes the form of pairwise constraints that indi- cate the similarity/dissimilarity between the two points. In this paper, we propose a novel matrix factorization based approach for semi-supervised clustering. In addi- tion, we extend our algorithm to co-cluster the data sets of difierent types with constraints. Finally the experi- ments on UCI data sets and real world Bulletin Board Systems (BBS) data sets show the superiority of our proposed method.