Tournament fuzzy clustering algorithm with automatic cluster number estimation

Yasunori Endo, Shingo Yamaguchi

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)


The theory of tournament clustering algorithms is used to develop a new fuzzy clustering algorithm. In the past, work on fuzzy clustering has focused on the fuzzy C-means (FCM) approach. While this approach is more effective than the "hard clustering" approach, which makes no use of fuzzy theory, it has certain deficiencies: it cannot handle objective or subjective differences between individuals well, and it lacks essential capabilities such as the ability to recognize isolated data items. To resolve these problems, a tournament fuzzy clustering algorithm with automatic cluster number estimation (T-FCA-ACNE) is proposed. The algorithm includes a capability for cluster number estimation and can express subjective and objective differences between individuals. The validity of the new algorithm is demonstrated by tests with real data.

Original languageEnglish
Pages (from-to)46-59
Number of pages14
JournalElectronics and Communications in Japan, Part III: Fundamental Electronic Science (English translation of Denshi Tsushin Gakkai Ronbunshi)
Issue number2
Publication statusPublished - 1998 Feb
Externally publishedYes


  • Cluster number estimation
  • Fuzzy set
  • Membership function
  • Tournament clustering algorithm

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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