An Adaptive Hybrid PSO and GSA Algorithm for Association Rules Mining

  • Zhiping ZhouEmail author
  • Daowen Zhang
  • Ziwen Sun
  • Jiefeng Wang
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9483)


Association rule mining is an interesting topic to extract hidden knowledge in data mining. Particle Swarm Optimization(PSO) has been used to mine Association rules, but it suffers from easily falling into local optimum. Gravitational search algorithm(GSA) has high performance in searching the global optimum but it suffers from running slowly especially in the last iterations. In order to resolve the aforementioned problem, in this paper a new hybrid algorithm called A_PSOGSA is proposed for association rules mining. Firstly, it integrates PSO and GSA. To make the idea simpler, PSO will browse the search space in such away to cover most of its regions and the local exploration of each particle is computed by GSA search. Secondly, the acceleration coefficients are controlled dynamically with the population distribution information during the process of evolution in order to provide a better balance between the ability of global and local searching. The experiments verify the accuracy and the effectiveness of the algorithm in this paper compared with the other algorithms for mining association rules.


Association rules mining Particle Swarm Optimization Gravitational Search Algorithm Dynamically updating approach 



This work is supported by Jiangsu Province Joint Research Project Foundation(BY2013015-33) and Nature Science Foundation of Jiangsu Province(NO. BK20131107)


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Zhiping Zhou
    • 1
    Email author
  • Daowen Zhang
    • 1
  • Ziwen Sun
    • 1
  • Jiefeng Wang
    • 1
  1. 1.School of Internet of Things EngineeringJiangnan UniversityWuxiChina

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