An improved Clustering Algorithm based on Fuzzy C-means and Artificial Bee Colony Optimization
Jaspreet Kaur, K. A. Abdul Nazeer · 2018 International Conference on Computing, Power and Communication Technologies (GUCON) · 2018
Fuzzy C-means (FCM) is considered to be one of the most popular fuzzy clustering techniques. However, it has the limitation of producing bad results due to its search for solution easily getting trapped into local optima. To address this problem a hybrid of FCM and Artificial Bee Colony optimization was proposed in the literature which can give global optimal solutions. This algorithm is also found to have issues such as the dependence of the quality of the results on randomly selected initial cluster centers. Here we propose an improved clustering algorithm based on Fuzzy C-Means and Artificial Bee Colony optimization which involves better methods for finding initial centroids and determining the value of K, the number of clusters.