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International Journal of Scientific and Engineering Research
ISSN Online 2229-5518
ISSN Print: 2229-5518 6    
Website: http://www.ijser.org
scirp IJSER >> Volume 2, Issue 6, June 2011 Edition
Evolving Data Mining Algorithms on the Prevailing Crime Trend - An Intelligent Crime Prediction Model
Full Text(PDF, 3000)  PP.  
A. Malathi and Dr. S. Santhosh Baboo
Crime-patterns, clustering, data mining, law-enforcement, Apriori.
Crime is a behavior deviation from normal activity of the norms giving people losses and harms. Crimes are a social nuisance and cost our society dearly in several ways. In this paper we look at use of missing value and clustering algorithm for crime data using data mining. We will look at MV algorithm and Apriori 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. Crime prevention is a significant issue that people are dealing with for centuries. We also use semi-supervised learning technique in this paper for knowledge discovery from the crime records and to help increase the predictive accuracy.
1. Amarnathan, L.C. (2003) Technological Advancement: Implications for Crime, The Indian Police Journal, April June.

2. Abraham, T. and de Vel, O. (2006) Investigative profiling with computer forensic log data and association rules,"""" in Proceedings of the IEEE International Conference on Data Mining (ICDM'02), Pp. 11 – 18.

3. Brown, D.E. (1998) The regional crime analysis program (RECAP): A frame work for mining data to catch criminals,"""" in Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics, Vol. 3, Pp. 2848-2853.

4. Corcoran J.J., Wilson I.D. AND Ware J.A. (2003) Predicting the geo-temporal variations of crime and disorder, International Journal of Forecasting, Vol. 19, Pp.623–634.

5. David, G. (2006) Globalization and International Security: Have the Rules of the Game Changed?, Annual meeting of the International Studies Association, California, USA, http://www.allacademic.com/meta/ p98627_index.html.

6. de Bruin, J.S. , Cocx, T.K. , Kosters, W.A. , Laros, J. and Kok, J.N. (2006) Data mining approaches to criminal career analysis,” in Proceedings of the Sixth International Conference on Data Mining (ICDM’06), Pp. 171-177.

7. Hauck, R.V.Atabakhsh, H., Ongvasith, P., Gupta, H. and Chen, H. (2002) Using Coplink to Analyze Criminal-Justice Data, Computer, Volume 35 Issue 3, Pp. 30-37.

8. Keyvanpour, M.R., Javideh, M. and Ebrahimi, M.R. (2010) Detecting and investigating crime by means of data mining: a general crime matching framework, Procedia Computer Science, World Conference on Information Technology, Elsvier B.V., Vol. 3, Pp. 872-830.

9. Nath, S. (2007) Crime data mining, Advances and innovations in systems, K. Elleithy (ed.), Computing Sciences and Software Engineering, Pp. 405-409.

10. Senator, T.E., Goldberg, H.G., Wooton, J., Cottini, M.A., Khan, A.F.U., Klinger, C.D., Llamas, W.M., Marrone, M.P. and Wong, R.W.H. (1995) The Fin- CEN Artificial Intelligence System: Identifying Potential Money Laundering from Reports of Large Cash Transactions, AI Magazine, Vol.16, No. 4, Pp. 21-39.

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