Aiming at the problem that the existing evaluation index of fuzzy C-means does not involve the real geometric distribution structure and prior information of data sets, an improved evaluation index VCSC is proposed to accurately find clusters that match the natural distribution of data samples. This index defines the compactness measure by combining the sum of squared errors, membership degree weights and root number weights of cluster data, the minimum distance between cluster centers, membership degree and the distance between cluster centers and average cluster centers and the definition separation degree, and the combination measure is defined by combining the range of membership degree and the distribution of samples. The experimental results show that the proposed index can effectively evaluate the clustering results and accurately get the best number of clusters in the data.

%U http://cea.ceaj.org/EN/10.3778/j.issn.1002-8331.1901-0117