• DocumentCode
    2101676
  • Title

    Mining Negative and Positive Influence Rules Using Kullback-Leibler Divergence

  • Author

    Alachaher, Leila Nemmiche ; Guillaume, Sylvie

  • Author_Institution
    Lab. LIMOS, Univ. Blaise Pascal, Aubiere
  • fYear
    2007
  • fDate
    4-9 March 2007
  • Firstpage
    25
  • Lastpage
    25
  • Abstract
    This paper describes a new method for mining negative and positive quantitative influence rules based on a coordination between a statistical dissimilarity measure (Kullback Leibler divergence) and contingency tables. This coordination identifies the significant positive and negative correlations and enables pertinent influence rules extraction.
  • Keywords
    data mining; Kullback-Leibler divergence; influence rules extraction; negative quantitative influence rule mining; positive quantitative influence rule mining; Association rules; Data mining; Decision making; Itemsets; Stress; Transaction databases;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computing in the Global Information Technology, 2007. ICCGI 2007. International Multi-Conference on
  • Conference_Location
    Guadeloupe City
  • Print_ISBN
    0-7695-2798-1
  • Type

    conf

  • DOI
    10.1109/ICCGI.2007.38
  • Filename
    4137080
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2101676