Survey on Crime statistics and Detection Using Data Classification
Keywords:
Crime information report, statistics data analysis, Clustering; K-Means AlgorithmAbstract
Crimes will somehow influence organizations and institutions when occurred frequently in a society. Thus, it seems necessary to study reasons, factors and relations between occurrence of different crimes and finding the most appropriate ways to control and avoid more crimes. The main objective of this paper is to classify clustered crimes based on occurrence frequency during different years. Data mining is used extensively in terms of analysis, investigation and discovery of patterns for occurrence of different crimes. We applied a theoretical model based on data mining techniques such as clustering and classification to real crime dataset recorded by police in England and Wales within 1990 to 2011. We assigned weights to the features in order to improve the quality of the model and remove low value of them. The Genetic Algorithm (GA) is used for optimizing of Outlier Detection operator parameters using RapidMiner tool.
References
. MINGCHEN FENG , JIANGBIN ZHENG, JINCHANG REN , (Senior Member, IEEE), AMIR HUSSAIN , (Senior Member, IEEE), XIUXIU LI, YUE XI , AND QIAOYUAN LIU on Big Data Analytics and Mining for Effective Visualization and Trends Forecasting of Crime Data Received May 8, 2019, accepted July 9, 2019, date of publication July 22, 2019 Digital Object Identifier 10.1109/ACCESS.2019.2930410. [2] Deepika K.K, Smitha Vinod on “Crime analysis in India using data mining techniques” INTERNATIONAL JOURNAL OF ENGINEEEING & TECHNOLOGY
. International Journal of Advanced Computational Engineering and Networking, ISSN: 2320-2106, Volume-4, Issue-5, May.-
J. Han, and M. Kamber, ―Data mining: concepts and techniques,‖ 2nd Edition, Morgan Kaufmann Publisher, 2001.
S. Joshi, and B. Nigam, ―Categorizing the document using multi class classification in data mining,‖ International Conference on Computational Intelligence and Communication Systems, 2011.
T. Phyu, ―Survey of classification techniques in data mining,‖ Proceedings of the International Multi Conference of Engineers and Computer Scientists Vol. IIMECS 2009, March 18 - 20, 2009, Hong Kong.
S.B. Kim, H.C. Rim, D.S. Yook, and H.S. Lim, ―Effective Methods for Improving Naïve Bayes Text Classifiers,‖ In Proceeding of the 7th Pacific Rim International Conference on Artificial Intelligence, Vol.2417, 2002.
S. Sindhiya, and S. Gunasundari, ―A survey on Genetic algorithm based feature selection for disease diagnosis system,‖ IEEE International Conference on Computer Communication and Systems(ICCCS), Feb 20- 21, 2014, Chermai, INDIA.
P. Gera, and R. Vohra, ―Predicting Future Trends in City Crime Using Linear Regression,‖ IJCSMS (International Journal of Computer Science & Management Studies) Vol. 14, Issue 07Publishing Month: July 2014.
L. Ding et al., ―PerpSearch: an integrated crime detection system,‖ 2009 IEEE 161-163 ISI 2009, June 8-11, 2009, Richardson, TX, USA.
K. Bogahawatte, and S. Adikari, ―Intelligent criminal identification system,‖ IEEE 2013 The 8th International Conference on Computer Science & Education (ICCSE 2013) April 26-28, 2013. Colombo, Sri Lanka.
A. Babakura, N. Sulaiman, and M. Yusuf, ―Improved method of calssification algorithms for crime prediction,‖ International Symposium on Biometrics and Security Technologies (ISBAST) IEEE 2014. [15] S. Sathyadevan, and S. Gangadharan, ―Crime analysis and prediction using data mining,‖ IEEE 2014.
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