An application of particle swarm optimization algorithm to clustering analysis

被引：19
|
作者：
Kuo, R. J. [1 ]
Wang, M. J. [2 ]
Huang, T. W. [2 ]
机构：
[1] Natl Taiwan Univ Sci & Technol, Dept Ind Management, Taipei 106, Taiwan
[2] Natl Taipei Univ Technol, Dept Ind Engn & Management, Taipei 106, Taiwan
来源：
SOFT COMPUTING | 2011年 / 15卷 / 03期
关键词：
Clustering analysis; Particle swarm optimization algorithm; K-means; K-MEANS ALGORITHM; ORGANIZING FEATURE MAPS; NEURAL-NETWORK; INTEGRATION;
D O I：
10.1007/s00500-009-0539-5
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Particle swarm optimization algorithm (PSOA), which maintains a population of particles, where each particle represents a potential solution to an optimization problem, is a population-based stochastic search process. This study intends to integrate PSOA with K-means to cluster data. It is shown that PSOA can be employed to find the centroids of a user-specified number of clusters. The proposed PSOA is evaluated using four data sets, and compared to the performance of some other PSOA-based methods and K-means method. Computational results show that the proposed method has much potential. A real-world problem for order clustering also illustrates that the proposed method is quite promising.
引用
收藏
页码：533 / 542
页数：10
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