Abstract
Data mining is receiving more attention to find the underlying patterns in crime data. It is need to act quickly to reduce crime activity and find out the links between various available data sources. The government are continuing to call upon modern geographic information systems to find the more intensive area of crime in order to protect their communities and assets. Real time solutions can provide significant resources and push the capability of law enforcement closer to the pulse of criminal activity.
There are 3 algorithms to study the pattern of any point data and for better inferences and interpretation. In this study, Mean Algorithm using Linguistic variable finds the most occurred crime at particular location among different types of crime. Mean algorithm using crime find the location not shown by earlier algorithm where sensitivity of crime is high. Fuzzy associations rule algorithm on point data formulate the rules among the crimes is a novel means for knowledge discovery in the crime domain, supported by experimental results using Mapobject, VB and Google Map.
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Sridhar, R., Sathyraj, S.R., Balasubramaniam, S. (2012). Analysis and Pattern Deduction on Linguistic, Numeric Based Mean and Fuzzy Association Rule Algorithm on Any Geo-referenced Crime Point Data Integrated with Google Map. In: Deep, K., Nagar, A., Pant, M., Bansal, J. (eds) Proceedings of the International Conference on Soft Computing for Problem Solving (SocProS 2011) December 20-22, 2011. Advances in Intelligent and Soft Computing, vol 131. Springer, New Delhi. https://doi.org/10.1007/978-81-322-0491-6_2
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DOI: https://doi.org/10.1007/978-81-322-0491-6_2
Publisher Name: Springer, New Delhi
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