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An Experimental Study of Classification Algorithms for Crime Prediction
Classification is a well-known supervised learning technique in data mining. It is used to extract meaningful information from large datasets and can be effectively used for predicting unknown classes. In this research, classification is applied to a crime dataset to predict 'Crime Category' for different states of the United States of America. The crime dataset used in this research is real in nature, it was collected from socio-economic data from 1990 US Census, law enforcement data from the 1990 US LEMAS survey, and crime data from the 1995 FBI UCR. This paper compares the two different classification algorithms namely, Naïve Bayesian and Decision Tree for predicting 'Crime Category' for different states in USA. The results from the experiment showed that, Decision Tree algorithm out performed Naïve Bayesian algorithm and achieved 83.9519% Accuracy in predicting 'Crime Category' for different states of USA.
Keywords
Crime Prediction, Crime Category, Algorithm
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- Batchu V, Aravindhar D J et al., (2011). A classifica-tion based dependent approach for suppressing data, IJCA Proceedings on Wireless Information Networks & Business Information System (WINBIS 2012), Foundation of Computer Science (FCS).
- Cios J, Pedrycz W et al., (1998). Data Mining in Knowledge Discovery, Academic Publishers.
- Geenen P. L, van der Gaag L C et al., (2011). Constructing naive Bayesian classifiers for veterinary medicine: A case study in the clinical diagnosis of classical swine fever, Research in Veterinary Science, vol 91(1), 64–70.
- Hamou A, Simmons A et al., (2011). Cluster analysis of MR imaging in Alzheimer’s disease using decision tree refinement. International Journal of Artificial Intelligence, vol 6(S11), 90–99.
- Han J, and Kamber M (2006). Data mining: concepts and techniques, Morgan Kaufmann Publishers, San Francisco, CA.
- Kochar B, and Chhillar R (2012). An Effective Data Warehousing System for RFID using Novel Data Cleaning, Data Transformation and Loading Techniques. Arab Journal of Information Technology, vol 9(3), 208–216
- Kováč S (2012). Suitability analysis of data mining tools and methods, Bachelor’s Thesis.
- Kumar V, and Rathee N (2011). Knowledge discovery from database using an integration of clustering and classification, International Journal of Advanced Computer Science and Applications, vol 2(3), 29–32.
- Li G, and Wang Y (2012). A privacy-preserving classification method based on singular value decomposition, Arab Journal of Information Technology, vol 9(6), 529–534.
- Ngai E W T, Xiu L et al., (2009). Application of data mining techniques in customer relationship management: A literature review and classification, Expert Systems with Applications, vol 36(2), 2592–2602.
- Santhi P, and Bhaskaran V. M (2010). Performance of clustering algorithms in healthcare database, International Journal for Advances in Computer Science, vol 2(1), 26–31.
- Selvaraj S, and Natarajan J (2011). Microarray data analysis and mining tools, Bioinformation, vol 6(3), 95–99.
- UCI Machine Learning Repository (2012). Available from: http://archive.ics.uci.edu/ml/datasets.html
- Wahbeh A H, Al-Radaideh Q A, et al., (2011). A comparison study between data mining tools over some classification methods, International Journal of Advanced Computer Science and Applications, Special Issue, 18–26.
- WikiPedia (2012). WIKIPEDIA, Available from: http://en.wikipedia.org/wiki/List_of_U.S._states_by_area.
- Witten I, Frank E et al. (2011), Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann.
- Xu Y, Dong Z. Y et al., (2011) A decision tree-based on-line preventive control strategy for power system transient instability prevention, International Journal of Systems Science.
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