Scalable clustering by truncated fuzzy $c$-means
Shiyang Sima, Qiujun Lan, Guojun Gan · Big Data and Information Analytics · 2016
Most existing clustering algorithms are slow for dividing a large dataset into a large number of clusters. In this paper, we propose a truncated FCM algorithm to address this problem. The main idea behind our proposed algorithm is to keep only a small number of cluster centers during the iterative process of the FCM algorithm. Our numerical experiments on both synthetic and real datasets show that the proposed algorithm is much faster than the original FCM algorithm and the accuracy is comparable to that of the original FCM algorithm.