• DocumentCode
    2704790
  • Title

    Parallel mining of association rules with a Hopfield type neural network

  • Author

    Gaber, K. ; Bahi, M.J. ; El-Ghazawi, T.

  • Author_Institution
    LIAL, Ecole Centrale de Lille, France
  • fYear
    2000
  • fDate
    2000
  • Firstpage
    90
  • Lastpage
    93
  • Abstract
    Association rule mining (ARM) is one of the data mining problems receiving a great deal of attention in the database community. The main computation step in an ARM algorithm is frequent itemset discovery. In this paper, a frequent itemset discovery algorithm based on the Hopfield model is presented
  • Keywords
    Hopfield neural nets; data mining; very large databases; ARM algorithm; Hopfield neural network; data mining; frequent itemset discovery algorithm; large database; parallel association rule mining; Association rules; Data mining; Databases; Economic forecasting; Electronic mail; Hopfield neural networks; Itemsets; Iterative algorithms; Neural networks; Partitioning algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Tools with Artificial Intelligence, 2000. ICTAI 2000. Proceedings. 12th IEEE International Conference on
  • Conference_Location
    Vancouver, BC
  • ISSN
    1082-3409
  • Print_ISBN
    0-7695-0909-6
  • Type

    conf

  • DOI
    10.1109/TAI.2000.889851
  • Filename
    889851
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=2704790