An application of particle swarm optimization algorithm to clustering analysis

被引：20
|
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
相关论文
共 50 条
  • [1] An application of particle swarm optimization algorithm to clustering analysis
    R. J. Kuo
    M. J. Wang
    T. W. Huang
    Soft Computing, 2011, 15 : 533 - 542
  • [2] A hybrid particle swarm optimization algorithm for clustering analysis
    Marinakis, Yannis
    Marinaki, Magdalene
    Matsatsinis, Nikolaos
    DATA WAREHOUSING AND KNOWLEDGE DISCOVERY, PROCEEDINGS, 2007, 4654 : 241 - +
  • [3] Evolution of the population of a genetic algorithm using particle swarm optimization: application to clustering analysis
    Yannis Marinakis
    Magdalene Marinaki
    Nikolaos Matsatsinis
    Constantin Zopounidis
    Operational Research, 2009, 9 (1) : 105 - 120
  • [4] Application of a hybrid of genetic algorithm and particle swarm optimization algorithm for order clustering
    Kuo, R. J.
    Lin, L. M.
    DECISION SUPPORT SYSTEMS, 2010, 49 (04) : 451 - 462
  • [5] Particle swarm optimization algorithm with environmental factors for clustering analysis
    Song, Wei
    Ma, Wei
    Qiao, Yingying
    SOFT COMPUTING, 2017, 21 (02) : 283 - 293
  • [6] Particle swarm optimization algorithm with environmental factors for clustering analysis
    Wei Song
    Wei Ma
    Yingying Qiao
    Soft Computing, 2017, 21 : 283 - 293
  • [7] The Clustering Algorithm Based on Particle Swarm Optimization Algorithm
    Pei Zhenkui
    Hua Xia
    Han Jinfeng
    INTERNATIONAL CONFERENCE ON INTELLIGENT COMPUTATION TECHNOLOGY AND AUTOMATION, VOL 1, PROCEEDINGS, 2008, : 148 - 151
  • [8] A New Particle Swarm Optimization Algorithm for Clustering
    Xu, Xiangping
    Li, Jun
    2018 IEEE 14TH INTERNATIONAL CONFERENCE ON AUTOMATION SCIENCE AND ENGINEERING (CASE), 2018, : 768 - 773
  • [9] Automatic particle swarm optimization clustering algorithm
    Chen, Ching-Yi
    Feng, Hsuan-Ming
    Ye, Fun
    International Journal of Electrical Engineering, 2006, 13 (04): : 379 - 387
  • [10] The Buttressed Walls Problem: An Application of a Hybrid Clustering Particle Swarm Optimization Algorithm
    Garcia, Jose
    Marti, Jose, V
    Yepes, Victor
    MATHEMATICS, 2020, 8 (06)
12345