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Analysis and Pattern Deduction on Linguistic based Mean and Fuzzy Association Rule Algorithm on any Geo-referenced Crime Point Data

R. Sridhar, S.R. Sathyaraj, S. Balasubramaniam

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 is 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. 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 and VB. Mean algorithm using crime find the location not shown by earlier algorithm where sensitivity of crime is high.

Keywords


Fuzzy,k-means clustering

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References


Kuok C.M, Fu A, and Wong H, “Mining Fuzzy Association Rules in Databases,” ACM SIGMOD Record, 27(1), pp. 41-46, New York, NY, 1998.

Nath S, “Crime Pattern Detection Using Data Mining,” In Proceedings of the International Conference on Web Intelligence and Intelligent Agent Technology, pp. 41-44, 2006, Washington, D.C., IEEE Computer Society Press.

Srikant R and Agrawal R, “Mining Quantitative Association Rules in Large Relational Tables,” In Proceedings of the International Conference on Management of Data, Montreal, Quebec, Canada, pp.1-12, 1996.

Brown D., “The Regional Crime Analysis Program (RECAP): A Framework for Mining Data to Catch Criminals,” In Proceedings of the International Conference on Systems, Man, and Cybernetics, pp. 2848-2853, 1998.

Chau M., Xu J., and Chen H., “Extracting Meaningful Entities from Police Narrative Reports,” In Proceedings of the National Conference on Digital Government Research, pp. 1-5, 2002.

Chen H., Chung W., Xu J., Wang G., Qin Y., and Chau M., “Crime Data Mining: A General Framework and Some Examples,” Computer, 37(4), pp. 50-56, April 2004, Los Alamitos, CA, IEEE Computer Society Press.

de Bruin J., Cocx T., Kosters W., Laros J., and Kok J., “Data Mining Approaches to Criminal Career Analysis,” In Proceedings of the International Conference on Data Mining, pp. 171-177, 2006, Washington, D.C., IEEE Computer Society Press.

Dembsky J, “United States Regions,” Online. Available (October 2006): http://www.dembsky.net/regions/.

Hauck R., Atabakhsh H., P. Ongvasith, H. Gupta, and H. Chen, “Using COPLINK to Analyze Criminal-Justice Data,” Computer, 35(3), pp. 30-37, March 2002, Los Alamitos, CA.

Ku C, Iriberri A, and Leroy G, “Crime Information Extraction from Police and Witness Narrative Reports,” In Proceedings of the IEEE International Conference on Technologies for Homeland Security, pp. 193-198, May 2008, Boston, MA.


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