Enhanced Algorithms to Identify Change in Crime Patterns

A major challenge facing all law-enforcement and intelligence gathering organizations is accurately and efficiently analyzing the growing volumes of crime data. There has been an enormous increase in the crime in the recent past. The concern about national security has increased significantly since the 26/11 attacks. Crimes are a social nuisance and cost our society dearly in several ways. Here we look at use of missing value, clustering algorithm and Anomalies detection for a data mining approach to help predict the crimes patterns and speed up the process of solving crime. We will look at MV algorithm, DBScan and PAM outlier detection algorithm with some enhancements to aid in the process of filling the missing value and identification of crime patterns. We applied these techniques to real crime data. We can use semi-supervised learning technique here for knowledge discovery from the crime records and to help increase the predictive accuracy.