• DocumentCode
    984084
  • Title

    Crime data mining: a general framework and some examples

  • Author

    Chen, Hsinchun ; Chung, Wingyan ; Xu, Jennifer Jie ; Wang, Gang ; Qin, Yi ; Chau, Michael

  • Author_Institution
    Arizona Univ., Tucson, AZ, USA
  • Volume
    37
  • Issue
    4
  • fYear
    2004
  • fDate
    4/1/2004 12:00:00 AM
  • Firstpage
    50
  • Lastpage
    56
  • Abstract
    A major challenge facing all law-enforcement and intelligence-gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. Detecting cybercrime can likewise be difficult because busy network traffic and frequent online transactions generate large amounts of data, only a small portion of which relates to illegal activities. Data mining is a powerful tool that enables criminal investigators who may lack extensive training as data analysts to explore large databases quickly and efficiently. We present a general framework for crime data mining that draws on experience gained with the Coplink project, which researchers at the University of Arizona have been conducting in collaboration with the Tucson and Phoenix police departments since 1997.
  • Keywords
    computer crime; data mining; law administration; police data processing; crime data mining; cybercrime detection; intelligence-gathering organizations; law-enforcement organizations; network traffic; online transactions; Cities and towns; Computer crime; Costs; Data mining; Data security; Local government; Monitoring; National security; Pattern analysis; Terrorism;
  • fLanguage
    English
  • Journal_Title
    Computer
  • Publisher
    ieee
  • ISSN
    0018-9162
  • Type

    jour

  • DOI
    10.1109/MC.2004.1297301
  • Filename
    1297301