Fuzzy kernel clustering based on particle swarm optimization

Abstract

A novel fuzzy kernel clustering algorithm is presented based on Particle Swarm Optimization algorithm (PSO). The idea of the algorithm is firstly map the data in the original space to a high-dimensional feature space by using Mercer kernel functions where the data are expected to be more separable then perform Fuzzy C-means (FCM) in the high dimensional space. The iteration process is replaced by the PSO based on gradient descent of FCM in feature space, which makes the algorithm have a strong global searching capacity and avoids the local minimum problems of FCM. At the same time, FCM is no longer a large degree dependent on the initialization values. The results of simulation experiments show the feasibility and effectiveness of the kernel clustering algorithm based on PSO.