Semi-supervised Agglomerative Hierarchical Clustering with Ward Method Using Clusterwise Tolerance

Abstract

This paper presents a new semi-supervised agglomerative hierarchical clustering algorithm with ward method using clusterwise tolerance. Recently, semi-supervised clustering has been remarked and studied in many research fields. In semi-supervised clustering, must-link and cannot-link called pairwise constraints are frequently used in order to improve clustering properties. First, a clusterwise tolerance based pairwise constraints is introduced in order to handle must-link and cannotlink constraints. Next, a new semi-supervised agglomerative hierarchical clustering algorithm with ward method is constructed based on above discussions. Moreover, the effectiveness of proposed algorithms is verified through numerical examples.