Comparison and Weighted Summation Type of Fuzzy Cluster Validity Indices
Keywords:
fuzzy clustering, fuzzy c-means (FCM), cluster validity indices (CVIs), WSCVIAbstract
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.
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