Approach of clustering based on PSO & PAM algorithm

Computer Engineering and Applications ›› 2013, Vol. 49 ›› Issue (4): 149-152.

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Approach of clustering based on PSO & PAM algorithm

HUANG Xiang1，2, CAI Biye1, MENG Ying1

1.College of Computer and Communication Engineering, Changsha University of Science and Technology, Changsha 410114, China
2.Network Media Department, Hunan Mass Media Vocational Technical College, Changsha 410100, China

Online:2013-02-15 Published:2013-02-18

一种基于PSO&PAM的聚类算法

黄翔1，2，蔡碧野1，孟颖1

1.长沙理工大学计算机与通信工程学院，长沙 410114
2.湖南大众传媒职业技术学院网络传媒系，长沙 410100

Abstract

Abstract: PAM is the first K-medoids algorithm proposed by one of the algorithm is relatively robust, but PAM sensitive to initial value, easily falling into local convergence. PSO algorithm is used to optimize the PAM, a approach of clustering based on PSO and PAM algorithm is proposed, making full use of both PAM and the PSO for the advantages of different issues, to continuously update the PAM clustering center. Through the establishment of cluster validity function based on entropy, the performance of the hybrid clustering algorithm is evaluated. the UCI data test results show that the hybrid clustering method has high accuracy of clustering.

Key words: Partitioning Around Medoid（PAM）, Particle Swarm Optimization（PSO）, cluster analysis, validity function

摘要： PAM是最早提出的k-medoids算法之一，该算法比较健壮，比k-means算法鲁棒性更强，但是PAM对初始值敏感，易陷入局部收敛。利用PSO算法对PAM进行优化，提出一种基于PSO和PAM的聚类方法，充分利用PAM和PSO两者对于不同问题的优势，来不断地更新PAM的聚类中心。通过建立基于熵的聚类有效性函数，对混合聚类算法的性能进行客观评价。从来自UCI的数据的测试结果表明，这种混合聚类的方法有较高的聚类正确率。

关键词: PAM算法, 粒子群优化算法, 聚类分析, 有效性函数

HUANG Xiang1，2, CAI Biye1, MENG Ying1. Approach of clustering based on PSO & PAM algorithm[J]. Computer Engineering and Applications, 2013, 49(4): 149-152.

黄翔1，2，蔡碧野1，孟颖1. 一种基于PSO&PAM的聚类算法[J]. 计算机工程与应用, 2013, 49(4): 149-152.

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Approach of clustering based on PSO & PAM algorithm

一种基于PSO&PAM的聚类算法

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