Abstract
Recently, semi-supervised clustering has been remarked 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.
In this paper, we will propose a way to handle must-link and cannot-link constraints by using clusterwise tolerance and
construct semi-supervised fuzzy c-means clustering algorithm. First, the concept of pairwise constraints and clusterwise
tolerance are introduced. Second, the optimization problem of proposed semi-supervised fuzzy c-means clustering using
clusterwise tolerance is formulated. Third, a new clustering algorithm is constructed based on the above discussions.
Finally, the effectiveness of proposed algorithm is verified through numerical examples.
