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 :
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