• DocumentCode
    3451994
  • Title

    Quantitative Association Rules Mining Method Based on Trapezium Cloud Model

  • Author

    Zhao-hong Wang

  • Author_Institution
    Dept. of Comput. Sci. & Technol., Weifang Univ., Weifang, China
  • fYear
    2010
  • fDate
    27-28 Nov. 2010
  • Firstpage
    1
  • Lastpage
    4
  • Abstract
    The quantitative association rules mining method is difficult for their values are too large. The usual means is dividing quantitative Data to discrete conception. The trapezium Cloud model combines ambiguity and randomness organically to fit the real world objectively, divide quantitative Data with trapezium Cloud model to create concepts, the concept cluster within one class, and separated with each other. So the quantitative Data can be transforms to Boolean data well, the Boolean data can be mined by the mature Boolean association rules mining method.
  • Keywords
    Boolean functions; cloud computing; data mining; pattern clustering; set theory; Boolean data; conception division; quantitative association rules mining; trapezium cloud model; Association rules; Clouds; Data models; Distribution functions; Entropy; Mathematical model;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Database Technology and Applications (DBTA), 2010 2nd International Workshop on
  • Conference_Location
    Wuhan
  • Print_ISBN
    978-1-4244-6975-8
  • Electronic_ISBN
    978-1-4244-6977-2
  • Type

    conf

  • DOI
    10.1109/DBTA.2010.5658963
  • Filename
    5658963