Host: Japan Society for Fuzzy Theory and Intelligent Informatics (SOFT)
Recently, semi-supervised clustering has been remarked and discussed in many research fields. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link are frequently used in order to improve clustering properties by using prior knowledges or informations. In this paper, we will propose a semi-supervised clustering by using clusterwise tolerance and pairwise constraints. First, the concept of clusterwise tolerance and pairwise constraints are introduced. Second, the optimization problem of fuzzy c-means clustering using clusterwise tolerance based pairwise constraints is formulated. Third, a new clustering algorithm is constructed based on the above discussions. Finally, the features of proposed algorithm is verified through numerical examples.
