• DocumentCode
    2851764
  • Title

    Relational peculiarity oriented data mining

  • Author

    Zhong, Ning ; Liu, Chunnian ; Yao, Y.Y. ; Ohshima, Muneaki ; Huang, Mingxin ; Huang, Jiajin

  • Author_Institution
    Dept. of Inf. Eng., Maebashi Inst. of Technol., Japan
  • fYear
    2004
  • fDate
    1-4 Nov. 2004
  • Firstpage
    575
  • Lastpage
    578
  • Abstract
    Peculiarity rules are a new type of interesting rules which can be discovered by searching the relevance among peculiar data. A main task of mining peculiarity rules is the identification of peculiarity. Traditional methods of finding peculiar data are attribute-based approaches. This paper extends peculiarity oriented mining to relational peculiarity oriented mining. Peculiar data are identified on record level, and peculiar rules are mined and explained in a relational mining framework. The results from preliminary experiments show that relational peculiarity oriented mining is very effective.
  • Keywords
    data mining; relational databases; attribute-based approach; peculiarity identification; peculiarity rules; relational peculiarity oriented data mining; Brain modeling; Computer science; Data engineering; Data mining; Educational institutions; Image databases; Laboratories; Learning systems; Logic programming; Relational databases;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Data Mining, 2004. ICDM '04. Fourth IEEE International Conference on
  • Print_ISBN
    0-7695-2142-8
  • Type

    conf

  • DOI
    10.1109/ICDM.2004.10008
  • Filename
    1410364