• DocumentCode
    3281463
  • Title

    AFOPT algorithm for multilevel databases

  • Author

    Gyorodi, Robert ; Gyorodi, Cornelia ; Pater, Mirela ; Boc, Ovidiu ; David, Zoltan

  • Author_Institution
    Dept. of Comput. Sci., Oradea Univ., Romania
  • fYear
    2005
  • fDate
    25-29 Sept. 2005
  • Abstract
    With the widespread computerization in business, government, and science, the efficient and effective discovery of interesting information from large databases becomes essential. Previous studies on data mining have been focused on the discovery of knowledge at single conceptual level, either at the primitive level or at a rather high conceptual level. This paper presents an algorithm based on the AFOPT algorithm for multilevel databases that uses the benefits of multileveled databases, by using the information gained by studying items from one concept level for the study of the items from the following concept levels.
  • Keywords
    data mining; very large databases; AFOPT algorithm; data mining; knowledge discovery; large databases; multilevel databases; Association rules; Computer science; Data mining; Government; Informatics; Itemsets; Machine learning; Pattern matching; Performance gain; Transaction databases;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Symbolic and Numeric Algorithms for Scientific Computing, 2005. SYNASC 2005. Seventh International Symposium on
  • Print_ISBN
    0-7695-2453-2
  • Type

    conf

  • DOI
    10.1109/SYNASC.2005.18
  • Filename
    1595839