Hybrid chemical reaction based metaheuristic with fuzzy c-means algorithm for optimal cluster analysis

Hybridization of two or more algorithms has always been a keen interest of research due to the quality of improvement in searching capability. Taking the positive insights of both the algorithms, the developed hybrid algorithm tries to minimize the substantial limitations. Clustering is an unsupervised learning method, which groups the data according to their similar or dissimilar properties. Fuzzy c-means (FCM) is one of the popularly used clustering algorithms and performs better as compared to other clustering techniques such as k-means. However, FCM possesses certain limitations such as premature trapping at local minima and high sensitivity to the cluster center initialization. Taking these issues into consideration, this research proposes a novel hybrid approach of FCM with a recently developed chemical based metaheuristic for obtaining optimal cluster centers. The performance of the proposed approach is compared in terms of cluster fitness values, inter-cluster distance and intra-cluster distance with other evolutionary and swarm optimization based approaches. A rigorous experimentation is simulated and experimental result reveals that the proposed hybrid approach is performing better as compared to other approaches.