The Use of Data Mining Techniques in Operational Crime Fighting

1. Abstract This paper looks at the application of data mining techniques, principally the Multi-Layer Perceptron and Self Organising Map, to the recognition of burglary offences committed by a network of offenders. The aim is to suggest a list of currently undetected crimes that may be attributed to one or more members of the network and improve on the time taken to complete the task manually and the relevancy of the list of crimes. The data was drawn from 4 years of burglary offences committed within an area of the West Midlands Police. It was encoded from text by a small team of specialists working to a well defined protocol and analysed using the above techniques contained within the data mining workbench of SPSS/Clementine. Within minutes, 3 months of undetected crimes were analysed through the Clementine stream producing a list of offences that may be attributed to the network of offenders. The results were analysed by 2 police analysts not associated with the development process who determined that 85% of the nominated crimes could be attributed to the network of offenders. To produce a manual list would take between 11⁄2 and 2 hours and be between 5% and 10% accurate.