The Research on Association Rules Mining with Co-evolution Algorithm in High Dimensional Data

  • Wei Lou
  • Lei Zhu
  • Limin Yan
Part of the Communications in Computer and Information Science book series (CCIS, volume 324)


This paper adopts a co-evolution algorithm, which utilizes improved genetic algorithm and particle swarm optimization algorithm to iterate two populations simultaneously. Meanwhile, the mechanism of information interaction between these two populations is introduced. Finally, experiments and application have been made to prove that on the premise of acceptable time complexity, not only does the co-evolution algorithm inherit the superiority of traditional genetic algorithm such as reducing the number of scanning the database effectively and generating small-scale candidate item sets, but also avoid the phenomenon of premature through comparing the properties of co-evolution algorithm, traditional genetic algorithm and particle swarm optimization algorithm when used in association rules mining. High quality association rules can be found when adopted the co-evolution algorithm, especially in high-dimension database.


Association rules mining Co-evolution Genetic algorithm (GA) Particle swarm optimization (PSO) algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Wei Lou
    • 1
  • Lei Zhu
    • 1
  • Limin Yan
    • 1
  1. 1.School of Mechatronics Engineering and AutomationShanghai UniversityShanghaiChina

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