• DocumentCode
    2484550
  • Title

    Unsupervised Abnormal Nodes Detection in Semantic Graphs

  • Author

    Sajane, Priya S. ; Sonavane, Shefali P.

  • Author_Institution
    Dept. of Comput. Sci. & Eng., Walchand Coll. of Eng., Sangli, India
  • fYear
    2011
  • fDate
    3-5 June 2011
  • Firstpage
    642
  • Lastpage
    645
  • Abstract
    Now a days, homeland security is the most concerned issue for every country. But identification of abnormal entities in large dataset is an important problem in case of homeland security. Though there exist methods of data mining and social network analysis which focus on finding pattern or central nodes from networks or numerical datasets. But none of them make use of semantic information associated with the network. In this paper we describe an unsupervised framework to identify abnormal instances.
  • Keywords
    data mining; graph theory; national security; public administration; security of data; social networking (online); data mining; homeland security; semantic graphs; social network analysis; unsupervised abnormal nodes detection; Bibliographies; Computer science; Knowledge based systems; Organizations; Peer to peer computing; Semantics; Terrorism; Abnormal entities; data mining; semantic graphs;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Communication Systems and Network Technologies (CSNT), 2011 International Conference on
  • Conference_Location
    Katra, Jammu
  • Print_ISBN
    978-1-4577-0543-4
  • Electronic_ISBN
    978-0-7695-4437-3
  • Type

    conf

  • DOI
    10.1109/CSNT.2011.138
  • Filename
    5966528