• DocumentCode
    526802
  • Title

    BitTableAC: Associative classification algorithm based on BitTable

  • Author

    Dong, Jie ; Lian, Jie

  • Author_Institution
    Fac. of Electron. Inf. & Electr. Eng., Dalian Univ. of Technol., Dalian, China
  • fYear
    2010
  • fDate
    13-15 Aug. 2010
  • Firstpage
    529
  • Lastpage
    532
  • Abstract
    This paper presents a new associative classification algorithm based on BitTable, i.e., associative classification algorithm based on BitTable (BitTableAC). BitTableAC employs BitTable to mine association rules efficiently, and fuzzy c-means (FCM) to partition quantitative attributes. It also adopts a new jointing and pruning technique to generate useful candidate itemsets directly. The experiments on datasets from UCI Machine Learning Repository demonstrate that the proposed algorithm performs well in comparison with other classification algorithms.
  • Keywords
    data mining; learning (artificial intelligence); pattern classification; BitTableAC; UCI machine learning repository; association rules mining; associative classification algorithm; fuzzy c-means; jointing technique; pruning technique; Accuracy; Algorithm design and analysis; Association rules; Classification algorithms; Itemsets;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Control and Information Processing (ICICIP), 2010 International Conference on
  • Conference_Location
    Dalian
  • Print_ISBN
    978-1-4244-7047-1
  • Type

    conf

  • DOI
    10.1109/ICICIP.2010.5565267
  • Filename
    5565267
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=526802