• DocumentCode
    3065769
  • Title

    The Strategy of Mining Association Rule Based on Cloud Computing

  • Author

    Li, Lingjuan ; Zhang, Min

  • Author_Institution
    Coll. of Comput., Nanjing Univ. of Posts & Telecommun., Nanjing, China
  • fYear
    2011
  • fDate
    29-31 July 2011
  • Firstpage
    475
  • Lastpage
    478
  • Abstract
    Cloud computing provides cheap and efficient solutions of storing and analyzing mass data. It is very important to research the data mining strategy based on cloud computing from the theoretical view and practical view. In this paper, the strategy of mining association rules in cloud computing environment is focused on. Firstly, cloud computing, Hadoop, MapReduce programming model, Apriori algorithm and parallel association rule mining algorithm are introduced. Then, a parallel association rule mining strategy adapting to the cloud computing environment is designed. It includes data set division method, data set allocation method, improved Apriori algorithm, and the implementation procedure of the improved Apriori algorithm on MapReduce. Finally, the Hadoop platform is built and the experiment for testing performance of the strategy as well as the improved algorithm has been done. The results show that the strategy designed in this paper can archive higher efficiency when doing frequent item set mining in cloud computing environment.
  • Keywords
    cloud computing; data analysis; data mining; parallel algorithms; Hadoop; MapReduce programming model; cloud computing; data analysis; data mining strategy; data set allocation method; data set division method; data storage; improved Apriori algorithm; parallel association rule mining algorithm; Algorithm design and analysis; Association rules; Cloud computing; Itemsets; Partitioning algorithms; Random access memory; Apriori; Association Rule Mining; Cloud Computing; MapReduce; Parallel;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Business Computing and Global Informatization (BCGIN), 2011 International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-1-4577-0788-9
  • Electronic_ISBN
    978-0-7695-4464-9
  • Type

    conf

  • DOI
    10.1109/BCGIn.2011.125
  • Filename
    6003927