• DocumentCode
    3347685
  • Title

    An Algorithm for Mining Association Rules Based on Improved Genetic Algorithm and its Application

  • Author

    Guo, Hong ; Zhou, Ya

  • Author_Institution
    Sch. of Comput. & Control, Guilin Univ. of Electron. Technol., Guilin, China
  • fYear
    2009
  • fDate
    14-17 Oct. 2009
  • Firstpage
    117
  • Lastpage
    120
  • Abstract
    Genetic algorithm is an important algorithm of association rule mining. However, there is some issues that genetic algorithm easy to lead prematuring convergence and into the plight of local optimum, or convergence too much time and consume a large amount of time to search. For resolving this issues, the paper improves the algorithm through adopting an adaptive mutation rate and improving the methods of individual choice, and the improved genetic algorithm that applies to the mining association rules. The simulating experiments show that the improved genetic algorithm reduces the cost of computing, and improve the efficiency of association rule mining.
  • Keywords
    data mining; genetic algorithms; adaptive mutation rate; association rule mining; improved genetic algorithm; individual choice methods; premature convergence; Association rules; Biological cells; Biology computing; Computational modeling; Data mining; Evolution (biology); Genetic algorithms; Genetic mutations; Itemsets; Transaction databases; Apriori; AssociationRule; Data Mining; Genetic Algorithm; PrematureConvergence;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Genetic and Evolutionary Computing, 2009. WGEC '09. 3rd International Conference on
  • Conference_Location
    Guilin
  • Print_ISBN
    978-0-7695-3899-0
  • Type

    conf

  • DOI
    10.1109/WGEC.2009.15
  • Filename
    5402932