A New Cluster Analysis Based on Combinatorial Particle Swarm Optimization Algorithm

Inspired by the swarm intelligence in self-organizing behavior of real Particle Swarm Optimization various Particle Swarm Optimization algorithms were proposed recently for many research fields in data mining such as clustering Compared with the previous clustering approaches such as K-means the main advantage of Particle Swarm Optimization based clustering algorithms is that no additional information is needed such as the initial partitioning of the data or the number of clusters In this paper, we discuss the clustering analysis way by a combination of advantages of particle swarm optimization in the clustering, since Particle Particle Swarm Optimization has the good global searching quickly. Firstly, the center and number of clustering are determined by using the Particle Swarm Optimization, and then the above clustering results are optimized by the K-means algorithm combining with the optimization algorithm. The simulated experiments show that the combining algorithm is obviously superior to some common clustering algorithms since it has obvious advantage in optimization capacity, more efficient and more robust than previous research such as the classical K-means clustering algorithm.