• DocumentCode
    2606081
  • Title

    An Improved Apriori Algorithm Based on Association Analysis

  • Author

    Jia, Yubo ; Xia, Guanghu ; Fan, Hongdan ; Zhang, Qian ; Li, Xu

  • fYear
    2012
  • fDate
    21-24 Oct. 2012
  • Firstpage
    208
  • Lastpage
    211
  • Abstract
    Association Rules Mining is an important branch of Data Mining Technology, of which Apriori Algorithm is the most influential and classic one. After discussing and analyzing the basic concept of Association Rules Mining, this paper proposes an improved algorithm based on a combination of Data Division and Dynamic Item sets Counting. Analysis of the improved algorithm proves that it can effectively improve the performance of Data Mining.
  • Keywords
    data mining; performance evaluation; association analysis; association rule mining; data division; data mining technology; dynamic itemsets counting; improved apriori algorithm; performance improvement; Algorithm design and analysis; Association rules; Heuristic algorithms; Itemsets; Apriori Algorithm; Association Rules Mining; Data Division; Data Mining; Dynamic Itemsets Counting;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Networking and Distributed Computing (ICNDC), 2012 Third International Conference on
  • Conference_Location
    Hangzhou
  • ISSN
    2165-5006
  • Print_ISBN
    978-1-4673-2858-6
  • Type

    conf

  • DOI
    10.1109/ICNDC.2012.56
  • Filename
    6386683
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2606081