Particle swarm optimization methods for data clustering

This paper discusses the application of particle swarm optimization (PSO) to data clustering. Four different methods of PSO are tested on six test data sets and compared to k-means and fuzzy c-means. The four PSO methods, combinations of the constriction method, inertia, and the predator-prey method all out-perform k-means and fuzzy c-means in all test cases to varying degrees in terms of quantization error.