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
Link To Document