Semi-supervised agglomerative hierarchical clustering with ward method using clusterwise tolerance

Authors:
Yukihiro Hamasuna;Yasunori Endo;Sadaaki Miyamoto
Affiliations:
Department of Informatics, School of Science and Engineering, Kinki University, Higashi Osaka, Osaka, Japan;Department of Risk Engineering, Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan;Department of Risk Engineering, Systems and Information Engineering, University of Tsukuba, Tsukuba, Ibaraki, Japan
Venue:
MDAI'11 Proceedings of the 8th international conference on Modeling decisions for artificial intelligence
Year:
2011

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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.