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

  • Yukihiro Hamasuna
  • Yasunori Endo
  • Sadaaki Miyamoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6820)

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 cannot-link 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.

Keywords

semi-supervised clustering pairwise constraints agglomerative hierarchical clustering ward method clusterwise tolerance 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yukihiro Hamasuna
    • 1
  • Yasunori Endo
    • 2
  • Sadaaki Miyamoto
    • 2
  1. 1.Department of Informatics, School of Science and EngineeringKinki UniversityHigashi OsakaJapan
  2. 2.Department of Risk Engineering, Systems and Information EngineeringUniversity of TsukubaTsukubaJapan

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