Comparison of Semi-Supervised Hierarchical Clustering Using Clusterwise Tolerance
Yukihiro Hamasuna* and Yasunori Endo**
*Department of Informatics, School of Science and Engineering, Kinki University, 3-4-1 Kowakae, Higashi-Osaka, Osaka 577-8502, Japan
**Faculty of Engineering, Information and Systems, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki 305-8573, Japan
This paper presents a new semi-supervised agglomerative hierarchical clustering algorithm with the ward method using clusterwise tolerance. Semi-supervised clustering has recently been noted and studied in many research fields. Must-link and cannot-link, called pairwise constraints, are frequently used in order to improve clustering properties in semi-supervised clustering. First, clusterwise tolerance based pairwise constraints are introduced in order to handle mustlink and cannot-link constraints. Next, a new semisupervised hierarchical clustering algorithm with the ward method is constructed based on the above discussions. The effectiveness of the proposed algorithms is, moreover, verified through numerical examples.
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