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Semi-supervised Agglomerative Hierarchical Clustering Using Clusterwise Tolerance Based Pairwise Constraints

  • Yukihiro Hamasuna
  • Yasunori Endo
  • Sadaaki Miyamoto
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6408)

Abstract

Recently, semi-supervised clustering has been remarked and discussed in many researches. In semi-supervised clustering, pairwise constraints, that is, must-link and cannot-link are frequently used in order to improve clustering results by using prior knowledges or informations. In this paper, we will propose a clusterwise tolerance based pairwise constraint. In addition, we will propose semi-supervised agglomerative hierarchical clustering algorithms with centroid method based on it. Moreover, we will show the effectiveness of proposed method through numerical examples.

Keywords

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

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Yukihiro Hamasuna
    • 1
    • 2
  • Yasunori Endo
    • 1
  • Sadaaki Miyamoto
    • 1
  1. 1.Department of Risk Engineering, Faculty of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan
  2. 2.Research Fellow of the Japan Society for the Promotion of ScienceJapan

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