• DocumentCode
    3414026
  • Title

    A Classification Algorithm Based on an Association Rule of Multiple Frequent Item-Sets

  • Author

    Liang, ZhiHeng

  • Author_Institution
    Software Coll., Shenyang Normal Univ., Shenyang, China
  • Volume
    3
  • fYear
    2009
  • fDate
    12-14 Aug. 2009
  • Firstpage
    278
  • Lastpage
    282
  • Abstract
    It is necessary to discrete datasets firstly if you want to data mining an association rule of datasets consisting of many categorical and numeric attributes by a traditional algorithm. However, in view of the versatility, the applications of the traditional algorithm are limited. This paper propose a new algorithm called ARMFI(Association Rule of Multiple Frequent Item-sets) which can data mining an Association Rule from datasets consisting of many categorical and numeric attributes directly and completely, and overcome disadvantage of the traditional algorithm. The result has been proofed that the ARMFI shows better performances than the traditional algorithm.
  • Keywords
    data mining; pattern classification; set theory; association rule; classification algorithm; data mining; multiple frequent item-set; numeric attribute; Association rules; Classification algorithms; Classification tree analysis; Data mining; Decision trees; Educational institutions; Electronic mail; Hybrid intelligent systems; Itemsets; Software algorithms; ARMFI; Classification; Data mining; Frequent Item-sets; Frequent Regions;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Hybrid Intelligent Systems, 2009. HIS '09. Ninth International Conference on
  • Conference_Location
    Shenyang
  • Print_ISBN
    978-0-7695-3745-0
  • Type

    conf

  • DOI
    10.1109/HIS.2009.271
  • Filename
    5254582