Tournament fuzzy clustering algorithm with automatic cluster number estimation

Yasunori Endo, Shingo Yamaguchi

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

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.

Keywords

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

ASJC Scopus subject areas

  • Electrical and Electronic Engineering

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