Journal of Jilin University Science Edition

Previous Articles     Next Articles

Stability ThresholdBased Affinity Propagation Algorithm

WANG Limin1, WANG Yizhang1, HAN Xuming2, HUANG Na3   

  1. 1. School of Management Science and Information Engineering, Jilin University of Finance and Economics,Changchun 130117, China; 2. School of
    Computer Science and Engineering, Changchun University of Technology, Changchun 130012, China; 3. School of Information Management and Engineering,Shanghai University of Finance and Economics, Shanghai 200433, China
  • Received:2014-05-13 Online:2014-11-26 Published:2014-12-11
  • Contact: HAN Xuming E-mail:hanxvming@163.com

Abstract:

In view of the performance of traditional affinity propagation algorithm greatly influenced by parameter P, a novel affinity propagation algorithm based on stability threshold was proposed. The improved algorithm can obtain the convergence of the real class number by stabilizing threshold, and then gain the corresponding parameter P. In order to improve the convergence speed, S function as convergence factor was applied to adjust damp parameter. In addition, it was successfully applied to the field of financial evaluation of listed companies. Simulation experimental results show that the improved clustering algorithm could obtain better precision and quicker convergence, and is obviously better than traditional affinity propagation clustering algorithm.

Key words: affinity propagation algorithm, stability threshold, convergence factor

CLC Number: 

  • TP301