Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (2): 190-193.DOI: 10.3778/j.issn.1002-8331.2011.02.057

• 图形、图像、模式识别 • Previous Articles     Next Articles

Application of Quantum-behaved Particle Swarm Optimization in projection pursuit clustering

ZHANG Qun,LEI Xiujuan,MA Qianzhi   

  1. School of Computer Science,Shaanxi Normal University,Xi’an 710062,China
  • Received:2009-06-10 Revised:2009-09-09 Online:2011-01-11 Published:2011-01-11
  • Contact: ZHANG Qun


张 群,雷秀娟,马千知   

  1. 陕西师范大学 计算机科学学院,西安 710062
  • 通讯作者: 张 群

Abstract: For solving the large calculation problem to high-dimensional data cluster,a novel projection pursuit cluster based on Quantum-behaved Particle Swarm Optimization(QPSO) algorithm which combines the global searching ability of QPSO and the dimension reduction capability of projection pursuit method are proposed in this paper.Compared with other algorithms,the simulation results of three kinds of testing data show that the proposed algorithm is much more efficient and accurate.

Key words: Quantum-behaved Particle Swarm Optimization(QPSO), projection pursuit, clustering

摘要: 提出基于量子粒子群的投影寻踪聚类算法,该算法将量子粒子群的全局搜索能力与投影寻踪对高维数据的降维能力相结合,有效解决了高维数据聚类计算量大效率低的问题。并将该算法应用于三种不同的测试数据,仿真实验结果表明该算法具有更好的效率,且提高了聚类效果,是解决高维聚类问题的一种有效方法。

关键词: 量子粒子群优化算法, 投影寻踪, 聚类

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