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JACIII Vol.16 No.7 pp. 819-824
doi: 10.20965/jaciii.2012.p0819
(2012)

Paper:

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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

Received:
December 1, 2011
Accepted:
September 25, 2012
Published:
November 20, 2012
Creative Commons License
Keywords:
semi-supervised clustering, hierarchical clustering, clusterwise tolerance, pairwise constraints
Abstract
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.
Cite this article as:
Y. Hamasuna and Y. Endo, “Comparison of Semi-Supervised Hierarchical Clustering Using Clusterwise Tolerance,” J. Adv. Comput. Intell. Intell. Inform., Vol.16 No.7, pp. 819-824, 2012.
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