A cluster validity index for fuzzy clustering

  • Authors:

  • Yunjie Zhang;Weina Wang;Xiaona Zhang;Yi Li

  • Affiliations:

  • Department of Mathematics, Dalian Maritime University, Dalian 116026, PR China;Department of Mathematics, Dalian Maritime University, Dalian 116026, PR China and Department of Mathematics, Jilin Institute of Chemical Technology, Jilin 132022, PR China;Department of Mathematics, Dalian Maritime University, Dalian 116026, PR China;Department of Computer and Information Engineering, Heilongjiang Institute of Science and Technology, Harbin 150027, PR China

  • Venue:
  • Information Sciences: an International Journal
  • Year:
  • 2008

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Abstract

A new cluster validity index is proposed for the validation of partitions of object data produced by the fuzzy c-means algorithm. The proposed validity index uses a variation measure and a separation measure between two fuzzy clusters. A good fuzzy partition is expected to have a low degree of variation and a large separation distance. Testing of the proposed index and nine previously formulated indices on well-known data sets shows the superior effectiveness and reliability of the proposed index in comparison to other indices and the robustness of the proposed index in noisy environments.