This paper looks at the application of data mining techniques, principally the Self Organising Map, to the recognition of burglary offences committed by an offender who, although part of a small network, appears to work on his own. The aim is to suggest a list of currently undetected crimes that may be attributed to him, improve on the time taken to complete the task manually and the relevancy of the list of crimes. The data was drawn from one year of burglary offences committed within the West Midlands Police area, encoded from text and analysed using techniques contained within the data mining workbench of SPSS/Clementine. The undetected crimes were analysed to produce a list of offences that may be attributed to the offender.
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