一种基于PSO的分割聚类算法  

PSO based partitional clustering algorithm

作　　者：张长胜[1] 孙吉贵[1] 崔妍[1] 杨凤芹[1] 

机构地区：[1]吉林大学计算机科学与技术学院,符号计算与知识工程教育部重点实验室,长春130012

出　　处：《吉林大学学报（工学版）》2008年第6期1371-1377,共7页Journal of Jilin University:Engineering and Technology Edition

基　　金：国家自然科学基金项目(60473003,60773097);教育部新世纪优秀人才支持计划基金项目(60273080);吉林省杰出青年基金项目(20030107);吉林省青年科研基金项目(20080107,20080617)

摘　　要：为了求得一个数据集的最优分割,提出了一种基于PSO的分割聚类算法-PKPSO,将PSO算法和K均值聚类算法有效地结合在一起,对群体中的候选解有选择地利用K均值算法做进一步优化以提高解的精度。通过对算法的分析,给出了控制参数选择依据。并将此算法与单独使用K均值、PSO算法及QPSO算法聚类进行比较,试验测试结果表明:PKPSO算法有更好的全局收敛性,不仅能有效地克服传统算法易陷入局部极小值的缺点,而且求得解的精度和算法的稳定性都明显优于其他方法。To obtain the optimal partition of a data set, a hybrid clustering algorithm, PKPSO, based on PSO is proposed. In the proposed PKPSO the PSO algorithm is effectively integrated with the K- means algorithm. Among the population, selected candidate solutions are further optimized to improve the accuracy by the K-means algorithm. By analyzing the algorithm, the criterions for control parameters selection are determined. Partional clustering result by the proposed PKPSO is compared with that by PSO or by K-means algorithm, and results show that the global convergent property of PKPSO is better than that of the other algorithms. The PKPSO can not only overcome the shortcoming of local minimum trapping of the K-means, but also the solution precision and algorithm stability are better than that of the other two algorithm.

关 键 词：人工智能 粒子群算法 分割聚类 K均值算法 惯性权重 

分 类 号：TP301.6[自动化与计算机技术—计算机系统结构]