Hard and Fuzzy c-means Algorithms with Pairwise Constraints by Non-metric Terms

  • Yasunori Endo
  • Naohiko Kinoshita
  • Kuniaki Iwakura
  • Yukihiro Hamasuna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8825)


Recently, semi-supervised clustering has been focused, e.g., Refs. [2–5]. The semi-supervised clustering algorithms improve clustering results by incorporating prior information with the unlabeled data. This paper proposes three new clustering algorithms with pairwise constraints by introducing non-metric term to objective functions of the well-known clustering algorithms. Moreover, its effectiveness is verified through some numerical examples.


Cluster Algorithm Cluster Center Unlabeled Data Normalize Mutual Information Rand Index 
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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yasunori Endo
    • 1
  • Naohiko Kinoshita
    • 2
  • Kuniaki Iwakura
    • 2
  • Yukihiro Hamasuna
    • 3
  1. 1.Faculty of Engineering, Information and SystemsUniversity of TsukubaTsukubaJapan
  2. 2.Graduate School of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan
  3. 3.Department of Informatics, School of Science and EngineeringKinki UniversityHigashiosakaJapan

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