JOURNAL OF SHANDONG UNIVERSITY (ENGINEERING SCIENCE) ›› 2011, Vol. 41 ›› Issue (2): 12-17.

• Articles • Previous Articles     Next Articles

A dynamic clustering algorithm based on a particle swarm
optimization with clonal selection

FAN Hui-lian1, LUO Yue-guo2, LI Xian-li2   

  1. 1. School of Mathematics and Computer; 2. Computer Network Information Center, Yangtze Normal University,
     Chongqing 408100, China
  • Received:2010-02-14 Online:2011-04-16 Published:2010-02-14

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Abstract:

In order to achieve cluster analysis with highdimensional and unknown number of clusters, a new clustering algorithm based on a particle swarm optimization algorithm(PSO) with clonal selection operator was proposed. Directed by the nature of PSO, this new algorithm could randomly search the clusters centers, and control the clone numbers and variation range by affinity. This algorithm could also avoid being trapped in local optima and could overcome being sensitive to  initialization. Experimental results on benchmark clustering problems showed that this new algorithm could adaptively determine the amount and the center’s positions of clustering. The results also showed that the average correct rate of the new algorithm was higher than the compared algorithm by  at least 7.0%.

Key words: clonal selection, particle swarm, clustering validity, affinity

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