Fuzzy c-Means Clustering with Mutual Relation Constraints

Construction of Two Types of Algorithms
  • Yasunori Endo
  • Yukihiro Hamasuna
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6881)

Abstract

Recently, semi-supervised clustering attracts many researchers’ interest. In particular, constraint-based semi-supervised clustering is focused and the constraints of must-link and cannot-link play very important role in the clustering. There are many kinds of relations as well as must-link or cannot-link and one of the most typical relations is the trade-off relation. Thus, in this paper we formulate the trade-off relation and propose a new “semi-supervised” concept called mutual relation. Moreover, we construct two types of new clustering algorithms with the mutual relation constraints based on the well-known and useful fuzzy c-means, called fuzzy c-means with the mutual relation constraints.

Keywords

Unlabeled Data Typical Relation Uncertain Data Mutual Relation Tolerance Vector 
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-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yasunori Endo
    • 1
  • Yukihiro Hamasuna
    • 2
  1. 1.Department of Risk Engineering, Faculty of Systems and Information EngineeringUniversity of TsukubaTsukubaJapan
  2. 2.Department of Informatics, School of Science and EngineeringKinki UniversityHigashiosakaJapan

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