On semi-supervised fuzzy c-means clustering with clusterwise tolerance by opposite criteria
Yukihiro Hamasuna, Yasunori Endo · 2011
The importance of semi-supervised clustering is to handle pairwise constraints as a prior knowledge. In this paper, we will propose a new semi-supervised fuzzy c-means clustering with clusterwise tolerance by opposite criteria. First, the concept of clusterwise tolerance and pairwise constraints are introduced. Second, the optimization problem of proposed method is formulated. Especially, must-link and cannot-link constraints are handled and introduced by opposite criteria in proposed method. Third, a new clustering algorithm is constructed based on the above discussions. Finally, the effectiveness of proposed algorithm is verified through numerical examples.