一种基于粒子群的聚类算法  

Clustering algorithm based on particle swarm optimization

作　　者：姚丽娟[1] 罗可[1] 孟颖[1] 

机构地区：[1]长沙理工大学计算机与通信工程学院,长沙410014

出　　处：《计算机工程与应用》2012年第13期150-153,175,共5页Computer Engineering and Applications

基　　金：国家自然科学基金(No.10926189;No.10871031);湖南省自然科学衡阳联合基金(No.10JJ8008);湖南省教育厅重点项目(No.10A015)

摘　　要：针对K-中心点算法对初始化敏感和容易陷入局部极值的缺点,提出一种基于粒子群算法和密度初始化改进的K-中心点聚类算法。该算法初始化时选择距离较远的k个候选范围作为k个聚类中心的选择范围,即粒子的初始值都在该k个范围内。通过粒子群算法优化聚类中心,以解决K-中心点算法因为聚类中心迭代计算较为复杂而导致的时间复杂度较高的问题。实验结果表明,该算法具有较高的正确率,较小的时间复杂度,综合性能更加稳定。After analyzing the disadvantages ot initialization sensitive anu total 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.

关 键 词：粒子群算法 K-中心点算法 密度初始化 聚类 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]