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Quantum-Behaved Particle Swarm Optimization Clustering Algorithm

  • Jun Sun
  • Wenbo Xu
  • Bin Ye
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)

Abstract

Quantum-behaved Particle Swarm Optimization (QPSO) is a novel optimization algorithm proposed in the previous work. Compared to the original Particle Swarm Optimization (PSO), QPSO is global convergent, while the PSO is not. This paper focuses on exploring the applicability of the QPSO to data clustering. Firstly, we introduce the K-means clustering algorithm and the concepts of PSO and QPSO. Then we present how to use the QPSO to cluster data vectors. After that, experiments are implemented to compare the performance of various clustering algorithms. The results show that the QPSO can generate good results in clustering data vectors with tolerable time consumption.

Keywords

Particle Swarm Optimization Cluster Algorithm Data Vector Quantization Error Centroid Vector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Jun Sun
    • 1
  • Wenbo Xu
    • 1
  • Bin Ye
    • 1
  1. 1.Center of Intelligent and High Performance Computing, School of Information TechnologySouthern Yangtze UniversityWuxiChina

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