• DocumentCode
    2826810
  • Title

    Quantitative Association Rules Mining Algorithm Based on Matrix

  • Author

    Liu, Huizhen ; Dai, Shangping ; Jiang, Hong

  • Author_Institution
    Dept. of Comput. Sci., Huazhong Normal Univ., Wuhan, China
  • fYear
    2009
  • fDate
    11-13 Dec. 2009
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    How to improve the efficiency of discovering the frequent item sets is a major problem in mining association rules. This paper analysised the idea and performance of the general quantitative association rules algorithm ,and put forward a quantitative association rules mining algorithm based on matrix, the new algorithm firstly transformed quantitative database into Boolean matrix ,then used boolean "and" operation to generate frequent item sets on matrix vector .It effectively solved the bottleneck of Apriori algorithm which iteratively produced frequent item sets in the general quantitative association rules algorithm . The results of experiments and analysis showed that the new algorithm effectively improved the efficiency of mining quantitative association rules.
  • Keywords
    Boolean algebra; data mining; matrix algebra; Boolean matrix; iteratively produced frequent item sets; matrix vector; quantitative association rules mining algorithm; Algorithm design and analysis; Artificial intelligence; Association rules; Computer science; Data mining; Iterative algorithms; Machine learning algorithms; Performance analysis; Relational databases; Transaction databases;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-4507-3
  • Electronic_ISBN
    978-1-4244-4507-3
  • Type

    conf

  • DOI
    10.1109/CISE.2009.5363896
  • Filename
    5363896