A hybrid elicit teaching learning based optimization with fuzzy c-means (ETLBO-FCM) algorithm for data clustering

Since its inception, Fuzzy c-means (FCM) technique has been widely used in data clustering. The advantages of FCM such as balancing of individual number of cluster points, drifting of small cluster centers to large neighboring cluster centers, and presence of fuzzy factor, make it more popular. However, early trapping at local minima and high sensitivity to the cluster center initialization are the major limitations of FCM. In this paper, a novel Elicit Teaching learning based optimization (ETLBO) approach has been incorporated with the Fuzzy c-means clustering algorithm to obtain the improved fitness values of the cluster centers. The simulation results of the proposed method have been compared with some other existing methods such as GA, PSO and IPSO. Experimental results show that the proposed approach is superior to the other methods in terms of their fitness value calculations.