A Membership Degree Refinement-Based Evolutionary Clustering Algorithm

Though FCM has already been widely used in clustering, its alternative calculation of the membership and prototype matrix causes a computational burden for large-scale data sets. An efficient algorithm, called accelerated fuzzy C-means (AFCM), is presented for reducing the computation time of FCM and FCM-based clustering algorithms. The proposed algorithm works by sampling initiation to generate better initial cluster centers, and motivated by the observation that there is the increasing trend for large membership degree values of data points at next iteration, updating cluster center using one step k-means for those data points with large membership degree values and only updating membership of data points with small values at next iteration. To verify the effectiveness of the proposed algorithm and improve the efficiency of EA for fuzzy clustering, AFCM also is applied to fuzzy clustering algorithms based on EAs, such as differential evolution (DE) and evolutionary programming (EP), to observe their performances. Experiments indicate that with a small loss of quality, the proposed algorithm can significantly reduce the computation time of clustering.