• DocumentCode
    1091467
  • Title

    Domain-Driven, Actionable Knowledge Discovery

  • Author

    Longbing Cao ; Chengqi Zhang

  • Author_Institution
    Univ. of Technol., Sydney
  • Volume
    22
  • Issue
    4
  • fYear
    2007
  • Firstpage
    78
  • Abstract
    Data mining increasingly faces complex challenges in the real-life world of business problems and needs. The gap between business expectations and R&D results in this area involves key aspects of the field, such as methodologies, targeted problems, pattern interestingness, and infrastructure support. Both researchers and practitioners are realizing the importance of domain knowledge to close this gap and develop actionable knowledge for real user needs.
  • Keywords
    data mining; business expectations; business intelligence; domain-driven data mining; knowledge discovery; Data mining; Data privacy; Data security; Data visualization; Government; Humans; Intelligent networks; Intelligent systems; Machine vision; Research and development; data mining; data models; database searching; knowledge engineering; visualization;
  • fLanguage
    English
  • Journal_Title
    Intelligent Systems, IEEE
  • Publisher
    ieee
  • ISSN
    1541-1672
  • Type

    jour

  • DOI
    10.1109/MIS.2007.67
  • Filename
    4287277