Web Intelligence and Intelligent Agent Technology, International Conference on (2006)
Hong Kong, China
Dec. 18, 2006 to Dec. 22, 2006
Shyam Varan Nath , Florida Atlantic University/Oracle Corporation, USA
Data mining can be used to model crime detection problems. Crimes are a social nuisance and cost our society dearly in several ways. Any research that can help in solving crimes faster will pay for itself. About 10% of the criminals commit about 50% of the crimes. Here we look at use of clustering algorithm for a data mining approach to help detect the crimes patterns and speed up the process of solving crime. We will look at k-means clustering with some enhancements to aid in the process of identification of crime patterns. We applied these techniques to real crime data from a sheriff?s office and validated our results. We also use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy. We also developed a weighting scheme for attributes here to deal with limitations of various out of the box clustering tools and techniques. This easy to implement data mining framework works with the geo-spatial plot of crime and helps to improve the productivity of the detectives and other law enforcement officers. It can also be applied for counter terrorism for homeland security.
Crime-patterns, clustering, data mining, k-means, law-enforcement, semi-supervised learning
Shyam Varan Nath, "Crime Pattern Detection Using Data Mining", Web Intelligence and Intelligent Agent Technology, International Conference on, vol. 00, no. , pp. 41-44, 2006, doi:10.1109/WI-IATW.2006.55