Data mining case study: modeling the behavior of offenders who commit serious sexual assaults
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Mining Clusters with Association Rules
IDA '99 Proceedings of the Third International Symposium on Advances in Intelligent Data Analysis
Extracting meaningful entities from police narrative reports
dg.o '02 Proceedings of the 2002 annual national conference on Digital government research
COPLINK: visualization for crime analysis DEMO
dg.o '03 Proceedings of the 2003 annual national conference on Digital government research
Visualizing criminal relationships: comparison of a hyperbolic tree and a hierarchical list
Decision Support Systems
An Early Warning System for the Prediction of Criminal Careers
MICAI '08 Proceedings of the 7th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence
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The information explosion has led to problems and possibilities in many areas of society, including that of law enforcement. In comparing individual criminal investigations on similarity, we seize one of the opportunities of the information surplus to determine what crimes may or may not have been committed by the same group of individuals. For this purpose we introduce a new distance measure that is specifically suited to the comparison between investigations that differ largely in terms of available intelligence. It employs an adaptation of the probability density function of the normal distribution to constitute this distance between all possible couples of investigations. We embed this distance measure in a four-step paradigm that extracts entities from a collection of documents and use it to transform a high dimensional vector table into input for a police operable tool. The eventual report is a two-dimensional representation of the distances between the various investigations and will assist the police force on the job to get a clearer picture of the current situation.