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Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation

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Abstract

Fuzzy c-means (FCM) algorithm is an important clustering method in pattern recognition, while the fuzziness parameter, m, in FCM algorithm is a key parameter that can significantly affect the result of clustering. Cluster validity index (CVI) is a kind of criterion function to validate the clustering results, thereby determining the optimal cluster number of a data set. From the perspective of cluster validation, we propose a novel method to select the optimal value of m in FCM, and four well-known CVIs, namely XB, VK, VT, and SC, for fuzzy clustering are used. In this method, the optimal value of m is determined when CVIs reach their minimum values. Experimental results on four synthetic data sets and four real data sets have demonstrated that the range of m is [2, 3.5] and the optimal interval is [2.5, 3].

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Authors and Affiliations

  1. School of Management, Hefei University of Technology, Hefei, 230009, China

    KaiLe Zhou, Chao Fu & ShanLin Yang

  2. Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education, Hefei, 230009, China

    KaiLe Zhou, Chao Fu & ShanLin Yang

Authors
  1. KaiLe Zhou
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  2. Chao Fu
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  3. ShanLin Yang
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Correspondence to KaiLe Zhou.

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Zhou, K., Fu, C. & Yang, S. Fuzziness parameter selection in fuzzy c-means: The perspective of cluster validation. Sci. China Inf. Sci. 57, 1–8 (2014). https://doi.org/10.1007/s11432-014-5146-0

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