吉林大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (2): 619-623.doi: 10.13229/j.cnki.jdxbgxb201502042

• Orignal Article • Previous Articles     Next Articles

Improved K-medoids algorithm based on genetic simulated annealing algorithm

HAN Xiao1,2,LIU Shu-fen1,XU Tian-qi1   

  1. 1.College of Computer Science and Technology, Jilin University, Changchun 130012, China;
    2.Editorial Department of Journal of Jilin University, Changchun 130012, China
  • Received:2014-01-13 Online:2015-04-01 Published:2015-04-01

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Abstract: Standard K-medoids algorithm has the disadvantages of easy-to-fall into local optima and the clustering effect is commonly influenced by the initial cluster centre. To overcome these shortcomings, a modified K-medoids algorithm is proposed which is based on the genetic simulated annealing algorithm. By combing the genetic algorithm and the annealing algorithm, the global search ability and convergence speed of the proposed algorithm are greatly improved. Comparison experiment results show that the modified algorithm can effectively overcome the shortcomings of the Standard K-medoids algorithm that the clustering efficiency, convergence speed and clustering quality are improved.

CLC Number: 

  • TP391
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