• DocumentCode
    3195719
  • Title

    Study on the Application of Multi-level Association Rules Based on Granular Computing

  • Author

    Shen, Yanguang ; Shen, Jing ; Fan, Yongjian

  • Author_Institution
    Sch. of Inf. & Electron. Eng., Hebei Univ. of Eng., Handan, China
  • Volume
    3
  • fYear
    2010
  • fDate
    11-12 May 2010
  • Firstpage
    564
  • Lastpage
    567
  • Abstract
    For the issue that classical association rules can not mine multi-level association rules, we proposed a multi-level association rule mining method based on binary information granules in granular computing and multiple minimum supports, and gave the definition of the support and confidence based on binary information granules. In this new association rules method, we can reduce the generation search space of frequent itemsets, extract multi-level association information(including cross-level information), and find more effective rules.
  • Keywords
    artificial intelligence; data mining; association rule mining method; binary information granules; cross-level information; granular computing; multilevel association rules; Agricultural products; Association rules; Automation; Data mining; Databases; Explosions; Frequency; Information processing; Itemsets; data mining; granular computing; multi-level association rule; multiple minimum supports;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Computation Technology and Automation (ICICTA), 2010 International Conference on
  • Conference_Location
    Changsha
  • Print_ISBN
    978-1-4244-7279-6
  • Electronic_ISBN
    978-1-4244-7280-2
  • Type

    conf

  • DOI
    10.1109/ICICTA.2010.656
  • Filename
    5522880