A new validity function of FCM clustering algorithm based on intra-class compactness and inter-class separation

Fuzzy C-means (FCM) clustering algorithm is a widely used method in data mining. However, there is a big limitation that the predefined number of clustering must be given. So it is very important to find an optimal number of clusters. Therefore, a new validity function of FCM clustering algorithm is proposed to verify the validity of the clustering results. This function is defined based on the intra-class compactness and inter-class separation from the fuzzy membership matrix, the data similarity between classes and the geometric structure of the data set, whose minimum value represents the optimal clustering partition result. The proposed clustering validity function and seven traditional clustering validity functions are experimentally verified on four artificial data sets and six UCI data sets. The simulation results show that the proposed validity function can obtain the optimal clustering number of the data set more accurately, and can still find the more accurate clustering number under the condition of changing the fuzzy weighted index, which has strong adaptability and robustness.

Automated summary

The FCM clustering algorithm requires a predefined number of clusters, but the proposed validity function can more accurately find the optimal clustering partition and has strong adaptability and robustness, even when changing the fuzzy weighted index.