Evolving Data Mining Algorithms on the Prevailing Crime Trend - An Intelligent Crime Prediction Model
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Full Text(PDF, 3000) PP.
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Author(s) |
A. Malathi and Dr. S. Santhosh Baboo |
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KEYWORDS |
Crime-patterns, clustering, data mining, law-enforcement, Apriori.
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ABSTRACT |
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.
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References |
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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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