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Crime Data Mining: A General Framework and Some Examples
April 2004 (vol. 37 no. 4)
pp. 50-56
Hsinchun Chen, University of Arizona
Wingyan Chung, University of Arizona
Jennifer Jie Xu, University of Arizona
Gang Wang, University of Arizona
Yi Qin, University of Arizona
Michael Chau, University of Hong Kong
The volume of crime data is increasing along with the incidence and complexity of crimes. Data mining is a powerful tool that criminal investigators who may lack extensive training as data analysts can use to explore large databases quickly and efficiently. The collaborative Coplink project between University of Arizona researchers and the Tucson and Phoenix police departments correlates data mining techniques applied in criminal and intelligence analysis with eight crime types. The framework has general applicability to crime and intelligence analysis because it encompasses all major crime types as well as both traditional and new intelligence-specific data mining techniques. Three case studies demonstrate the framework?s effectiveness.
Citation:
Hsinchun Chen, Wingyan Chung, Jennifer Jie Xu, Gang Wang, Yi Qin, Michael Chau, "Crime Data Mining: A General Framework and Some Examples," Computer, vol. 37, no. 4, pp. 50-56, April 2004, doi:10.1109/MC.2004.1297301
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