Journal of Computer Science and Technology

, Volume 21, Issue 1, pp 137–140 | Cite as

Novel Cluster Validity Index for FCM Algorithm

  • Jian YuEmail author
  • Cui-Xia Li


How to determine an appropriate number of clusters is very important when implementing a specific clustering algorithm, like c-means, fuzzy c-means (FCM). In the literature, most cluster validity indices are originated from partition or geometrical property of the data set. In this paper, the authors developed a novel cluster validity index for FCM, based on the optimality test of FCM. Unlike the previous cluster validity indices, this novel cluster validity index is inherent in FCM itself. Comparison experiments show that the stability index can be used as cluster validity index for the fuzzy c-means.


cluster validity optimality test FCM 


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Copyright information

© Springer Science + Business Media, Inc. 2006

Authors and Affiliations

  1. 1.Institute of Computer ScienceBeijing Jiaotong UniversityBeijingP.R. China

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