• DocumentCode
    597737
  • Title

    Partition document clustering using ontology approach

  • Author

    Punitha, S.C. ; Jayasree, R. ; Punithavalli, M.

  • Author_Institution
    Dept. of Comput. Sci., P.S.G.R. KrishnammalCollege for Women, Coimbatore, India
  • fYear
    2013
  • fDate
    4-6 Jan. 2013
  • Firstpage
    1
  • Lastpage
    5
  • Abstract
    Data mining is the extraction of hidden predictive information from large databases and it is a powerful new technology with great potential to help companies focus on the most important information in their data warehouses. Data mining tools predict future trends and behaviors, allowing businesses to make proactive, knowledge-driven decisions. In data mining there are two activities such as Classification and clustering [5]. Text clustering typically involves clustering in a high dimensional space, which appears difficult with regard to virtually all practical settings. The creation and deployment of knowledge repositories for managing, sharing, and reusing tacit knowledge within an organization has emerged as a prevalent approach in current knowledge management practices.
  • Keywords
    data mining; data warehouses; knowledge management; ontologies (artificial intelligence); pattern clustering; text analysis; data mining; data warehouses; hidden predictive information; knowledge management; knowledge repositories; large databases; ontology approach; partition document clustering; tacit knowledge; text clustering; Algorithm design and analysis; Clustering algorithms; Computers; Data mining; Educational institutions; Ontologies; Partitioning algorithms;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Computer Communication and Informatics (ICCCI), 2013 International Conference on
  • Conference_Location
    Coimbatore
  • Print_ISBN
    978-1-4673-2906-4
  • Type

    conf

  • DOI
    10.1109/ICCCI.2013.6466246
  • Filename
    6466246