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Novel Cluster Validity Index for FCM Algorithm

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Abstract

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.

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Authors and Affiliations

  1. Institute of Computer Science, Beijing Jiaotong University, Beijing, 100044, P.R. China

    Jian Yu & Cui-Xia Li

Authors
  1. Jian Yu
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  2. Cui-Xia Li
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Correspondence to Jian Yu.

Additional information

Supported by the National Natural Science Foundation of China under Grant No. 60303014.

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Yu, J., Li, CX. Novel Cluster Validity Index for FCM Algorithm. J Comput Sci Technol 21, 137–140 (2006). https://doi.org/10.1007/s11390-006-0137-2

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  • DOI: https://doi.org/10.1007/s11390-006-0137-2

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