Some pairwise constrained semi-supervised fuzzy c-means clustering algorithms

Yuchi Kanzawa, Yasunori Endo, Sadaaki Miyamoto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

6 Citations (Scopus)

Abstract

In this paper, some semi-supervised clustering methods are proposed with two types of pair constraints: two data have to be together in the same cluster, and two data have to be in different clusters, which are classified into two types: one is based on the standard fuzzy c-means algorithm and the other is on the entropy regularized one. First, the standard fuzzy c-means and the entropy regularized one are introduced. Second, a pairwise constrained semi-supervised fuzzy c means are introduced, which is derived from pairwise constrained competitive agglomeration. Third, some new optimization problem are proposed, which are derived from adding new loss function of memberships to the original optimization problem, respectively. Last, an iterative algorithm is proposed by solving the optimization problem.

Original languageEnglish
Title of host publicationModeling Decisions for Artificial Intelligence - 6th International Conference, MDAI 2009, Proceedings
Pages268-281
Number of pages14
DOIs
Publication statusPublished - 2009 Dec 1
Event6th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2009 - Awaji Island, Japan
Duration: 2009 Nov 302009 Dec 2

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5861 LNAI
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference6th International Conference on Modeling Decisions for Artificial Intelligence, MDAI 2009
CountryJapan
CityAwaji Island
Period09/11/3009/12/2

Keywords

  • Fuzzy c-means
  • Pairwise constraints
  • Semi-supervised clustering

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Kanzawa, Y., Endo, Y., & Miyamoto, S. (2009). Some pairwise constrained semi-supervised fuzzy c-means clustering algorithms. In Modeling Decisions for Artificial Intelligence - 6th International Conference, MDAI 2009, Proceedings (pp. 268-281). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 5861 LNAI). https://doi.org/10.1007/978-3-642-04820-3_25