Fuzzy cluster validation index based on inter-cluster proximity

  • Authors:
  • Dae-Won Kim;Kwang H. Lee;Doheon Lee

  • Affiliations:
  • Department of Electrical Engineering and Computer Science, KAIST, 373-1, Kusung-dong, Yusung-gu, Daejon 305701, South Korea;Department of Electrical Engineering and Computer Science, KAIST, 373-1, Kusung-dong, Yusung-gu, Daejon 305701, South Korea and Department of BioSystems, KAIST, 373-1, Kusung-dong, Yusung-gu, Daej ...;Department of BioSystems, KAIST, 373-1, Kusung-dong, Yusung-gu, Daejon 305701, South Korea

  • Venue:
  • Pattern Recognition Letters
  • Year:
  • 2003

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Abstract

A new cluster validity index is proposed for fuzzy partitions obtained from Fuzzy C-Means algorithm. The proposed validity index exploits an inter-cluster proximity between fuzzy clusters. The inter-cluster proximity is used to measure the degree of overlap between clusters. A low proximity value indicates well-partitioned clusters. The best fuzzy c-partition is obtained by minimizing the inter-cluster proximity with respect to c. Well-known data sets are tested to show the effectiveness and reliability of the proposed index.