Fuzzy C-means clustering algorithm for automatically determining the number of clusters
Zhihe Wang, Shuyan Wang, Hui Du, Hao Guo · 2020
Traditional fuzzy C-means (FCM) clustering algorithm is sensitive to initial clustering center, and the number of clusters need to be set artificially in advance. For these reasons, we propose an improved FCM algorithm (AMMF) that can determine the number of clusters automatically. Firstly, the proposed algorithm uses the affinity propagation clustering algorithm to obtain coarse number of clusters, which are taken as the upper limit of searching the best number of clusters. Secondly, by the improved maximum and minimum distance algorithm obtains some representative sample points as the initial clustering centers of the FCM algorithm. Lastly, we use Silhouette Coefficient to analyze the quality of clustering to determine the optimal number of clusters automatically. Experimental results show that the AMMF algorithm has significantly better clustering performance than other improved FCM based algorithms, and improves the stability of the clustering results.