KF-PSO: Hybridization of particle swarm optimization and kernel-based fuzzy C means algorithm

In recent times, clustering has been well known for various researchers due to various applications in most of the fields like, telecommunication, networking, biomedical domain and so on. So, various attempts have been already made by the researchers to develop a better algorithm for clustering. One of the procedures well known among the researchers is optimization that has been effectively utilized for clustering. In most of the clustering, the objective function is to minimize the intra cluster distance among the data points. Here, we have made significant trial in developing optimization based clustering algorithm utilizing kernel-based FCM and PSO algorithm. The objective function for minimization is taken from kernel-based FCM and the same objective is solved using PSO algorithm. The algorithm developed based on these scenarios are named as, KF-PSO. At first, the input data is given to PSO algorithm and the final best cluster centers are chosen from PSO algorithm useful for grouping based on the objective of the kernel clustering. Finally, the experimentation have been performed in various datasets and from the results, we have showed that the proposed hybrid algorithm achieved 98% and 90.5 % accuracy in iris and wine dataset.