Techniques for intuitionistic fuzzy kernel clustering based on particle swarm optimization

Abstract

The Intuitionistic fuzzy clustering algorithms are sensitive to the initial value, easy to fall into local optimum and have slow convergence speed. To overcome these shortages, the particle swarm optimization (PSO) algorithm with powerful ability of global search and quick convergence rate is applied to Intuitionistic fuzzy clustering. Firstly, PSO is used to optimize the initial clustering centers. Then, the approach of intuitionistic fuzzy kernel clustering based on PSO, namely PS-IFKCM, is proposed. Finally, experiments based on four measured datasets are carried out to illustrate the performance of the proposed method. Compared with results from FCM and IFKCM, PS-IFKCM is of great efficiency for classification.