Clustering algorithm based on density function and nichePSO

This paper introduces niching particle swarm optimization (nichePSO) into clustering analysis and puts forward a clustering algorithm which uses nichePSO to optimize density functions. Firstly, this paper improves main swarm training models and increases their ability of space searching. Secondly, the radius of sub-swarms is defined adaptively according to the actual clustering problem, which can be useful for the niches' forming and searching. At last, a novel method that distributes samples to the corresponding cluster is proposed. Numerical results illustrate that this algorithm based on the density function and nichePSO could cluster unbalanced density datasets into the correct clusters automatically and accurately.