关键词: 粒子群算法, K-中心点算法, 密度初始化, 聚类

Abstract: After analyzing the disadvantages of initialization sensitive and local maximum of the K-medians algorithm, this paper proposes a novel K-medians clustering based on Particle Swarm Optimization（PSO） algorithm and density initialization. The Initialization of the algorithm is that, it chooses k candidate ranges which are far apart as the selection range for the k cluster centers, that is, the initial values of the particles are included in the k ranges. Through PSO clustering center, to solve the problem of the K-medians algorithm caused by the cluster center iteration is more complex due to the time complexity is higher. Experimental results show that this algorithm has higher accuracy, smaller time complexity, and more stable overall performance.

Key words: Particle Swarm Optimization（PSO）, K-medians algorithm, density initialization, clustering