Comparison and Weighted Summation Type of Fuzzy Cluster Validity Indices

  • Kaile Zhou Hefei University of Technology
  • Shuai Ding Hefei University of Technology
  • Chao Fu School of Management Hefei University of Technology Hefei 230009, China
  • Shanlin Yang Hefei University of Technology


Finding the optimal cluster number and validating the partition resultsof a data set are difficult tasks since clustering is an unsupervised learning process.Cluster validity index (CVI) is a kind of criterion function for evaluating the clusteringresults and determining the optimal number of clusters. In this paper, we present anextensive comparison of ten well-known CVIs for fuzzy clustering. Then we extendtraditional single CVIs by introducing the weighted method and propose a weightedsummation type of CVI (WSCVI). Experiments on nine synthetic data sets and fourreal-world UCI data sets demonstrate that no one CVI performs better on all datasets than others. Nevertheless, the proposed WSCVI is more effective by properlysetting the weights.


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How to Cite
ZHOU, Kaile et al. Comparison and Weighted Summation Type of Fuzzy Cluster Validity Indices. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 9, n. 3, p. 370-378, apr. 2014. ISSN 1841-9844. Available at: <>. Date accessed: 13 july 2020. doi:


fuzzy clustering, fuzzy c-means (FCM), cluster validity indices (CVIs), WSCVI