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

页码：533 / 542

页数：10

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