Comparison and Weighted Summation Type of Fuzzy Cluster Validity Indices

Kaile Zhou, Shuai Ding, Chao Fu, Shanlin Yang


Finding the optimal cluster number and validating the partition results
of 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 clustering
results and determining the optimal number of clusters. In this paper, we present an
extensive comparison of ten well-known CVIs for fuzzy clustering. Then we extend
traditional single CVIs by introducing the weighted method and propose a weighted
summation type of CVI (WSCVI). Experiments on nine synthetic data sets and four
real-world UCI data sets demonstrate that no one CVI performs better on all data
sets than others. Nevertheless, the proposed WSCVI is more effective by properly
setting the weights.


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

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