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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)

Abstract

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.

Keywords

Cluster Algorithm Cluster Center Unlabeled Data Normalize Mutual Information Rand Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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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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