ICTACT Journal on Soft Computing

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Survey on Crime Analysis and Prediction Using Data Mining Techniques


Affiliations
1 Department of Computer Science and Engineering, Manonmaniam Sundaranar University, India
     

   Subscribe/Renew Journal


Data Mining is the procedure which includes evaluating and examining large pre-existing databases in order to generate new information which may be essential to the organization. The extraction of new information is predicted using the existing datasets. Many approaches for analysis and prediction in data mining had been performed. But, many few efforts has made in the criminology field. Many few have taken efforts for comparing the information all these approaches produce. The police stations and other similar criminal justice agencies hold many large databases of information which can be used to predict or analyze the criminal movements and criminal activity involvement in the society. The criminals can also be predicted based on the crime data. The main aim of this work is to perform a survey on the supervised learning and unsupervised learning techniques that has been applied towards criminal identification. This paper presents the survey on the Crime analysis and crime prediction using several Data Mining techniques.

Keywords

Criminology, Crime Analysis, Crime Prediction, Data Mining.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Mugdha Sharma, “Z-Crime: A Data Mining Tool for the Detection of Suspicious Criminal Activities based on the Decision Tree”, International Conference on Data Mining and Intelligent Computing, pp. 1-6, 2014.

  • Kaumalee Bogahawatte and Shalinda Adikari, “Intelligent Criminal Identification System”, Proceedings of 8th IEEE International Conference on Computer Science and Education, pp. 633-638, 2013.

  • Jyoti Agarwal, Renuka Nagpal and Rajni Sehgal, “Crime Analysis using K-Means Clustering”, International Journal of Computer Applications, Vol. 83, No. 4, pp. 1-4, 2013.

  • Rasoul Kiani, Siamak Mahdavi and Amin Keshavarzi, “Analysis and Prediction of Crimes by Clustering and Classification”, International Journal of Advanced Research in Artificial Intelligence, Vol. 4, No. 8, pp. 11-17, 2015.

  • Shiju Sathyadevan, M.S. Devan and S. Surya Gangadharan, “Crime Analysis and Prediction using Data Mining”, Proceedings of IEEE 1st International Conference on Networks and Soft Computing, pp. 406-412, 2014.

  • Yu-Yueh Huang, Cheng-Te Li and Shyh-Kang Jeng, “Mining Location-based Social Networks for Criminal Activity Prediction”, Proceedings of 24th IEEE International Conference on Wireless and Optical Communication, pp. 185-190, 2015.

  • Jeroen S. De Bruin, Tim K. Cocx, Walter A. Kosters, Jeroen F. J. Laros and Joost N. Kok, “Data Mining Approaches to Criminal Career Analysis”, Proceedings of 6th IEEE International Conference on Data Mining, pp. 1-7, 2006.

  • Ehab Hamdy, Ammar Adl, Aboul Ella Hassanien, Osman Hegazy and Tai-Hoon Kim, “Criminal Act Detection and Identification Model”, Proceedings of 7th International Conference on Advanced Communication and Networking, pp. 79-83, 2015.

  • Kamal Taha and Paul D. Yoo, “SIIMCO: A Forensic Investigation Tool for Identifying the Influential Members of a Criminal Organization”, IEEE Transactions on Information Forensics and Security, Vol. 11, No. 4, pp. 811-822, 2016.

  • Kevin Sheehy et al., “Evidence-based Analysis of Mentally 111 Individuals in the Criminal Justice System”, Proceedings of IEEE Systems and Information Engineering Design Symposium, pp. 250-254, 2016.

  • David J. Hand, Heikki Mannila and Padhraic Smyth, “Principles of Data Mining”, MIT Press, 2001.

  • 10 Emerging Technologies That Will Change Your World, Available at: http://www.rle.mit.edu/thz/documents/10_emerging_tech.pdf.

  • U. Fayyad, G. Piatetsky-Shapiro and P. Smyth, “The KDD Process for Extracting Useful Knowledge from Volumes of Data”, Communications of the ACM, Vol. 39, No. 11, pp. 27-34, 1996.

  • Illhoi Yoo, Patricia Alafaireet, Miroslav Marinov, Keila Pena-Hernandez, Rajitha Gopidi, Jia-Fu Chang and Lei Hua, “Data Mining in Healthcare and Biomedicine: A Survey of the Literature”, Journal of Medical Systems, Vol. 36, No. 4, pp. 2431-2448, 2011.

  • Shyam Varan Nath, “Crime Pattern Detection using Data Mining”, Proceedings of IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Workshops, pp. 1-4, 2006.

  • Hsinchun Chen, Wingyan Chung, Yi Qin, Michael Chau, Jennifer Jie Xu, Gang Wang, Rong Zheng and Homa Atabakhsh, “Crime Data Mining: An Overview and Case Studies”, Proceedings National Conference on Digital Government Research, pp. 1-5, 2003.

  • Tong Wang et al., “Learning to Detect Patterns of Crime”, Joint European Conference on Machine Learning and Knowledge Discovery in Databases, pp. 515-530, 2013.

  • Karl F. Schuessler and Donald R. Cressey, “Personality Characteristics of Criminals”, American Journal of Sociology, Vol. 55, No. 5, pp. 476-484, 1950.

  • H. Chen, W. Chung, J.J. Xu, G. Wang, Y. Qin and M. Chau, “Crime Data Mining: a General Framework and Some Examples”, Computer, Vol. 37, No. 4, pp. 50-56, 2004.

  • Chung-Hsien Yu, Max W. Ward, Melissa Morabito and Wei Ding, “Crime Forecasting using Data Mining Techniques”, Proceedings of 11th IEEE International Conference on Data Mining Workshops, pp. 779-786, 2011.

  • P. Thongtae and S. Srisuk, “An Analysis of Data Mining Applications in Crime Domain”, Proceedings of IEEE 8th International Conference on Computer and Information Technology Workshops, pp. 122-126, 2008.

  • Rizwan Iqbal, Masrah Azrifah Azmi Murad, Aida Mustapha, Payam Hassany Shariat Panahy and Nasim Khanahmadliravi, “An Experimental Study of Classification Algorithms for Crime Prediction”, Indian Journal of Science and Technology, Vol. 6, No. 3, pp. 4219-4225, 2013.

  • Shyam Varan Nath, “Crime Data Mining”, Proceedings of Advances and Innovations in Systems, Computing Sciences and Software Engineering, pp. 405-409, 2007.

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

  • Colleen McCue, “Data Mining and Predictive Analysis: Intelligence Gathering and Crime Analysis”, Butterworth-Heinemann, 2014.

  • Arunima S. Kumar and Raju K. Gopal, “Data Mining based Crime Investigation Systems: Taxonomy and Relevance”, Proceedings of Global Conference on IEEE Communication Technologies, pp. 850-853, 2015.

  • Manish Gupta, B. Chandra and M.P. Gupta, “Crime Data Mining for Indian Police Information System”, Journal of Crime, Vol. 2, No. 6, pp. 43-54, 2006.


Abstract Views: 694

PDF Views: 9




  • Survey on Crime Analysis and Prediction Using Data Mining Techniques

Abstract Views: 694  |  PDF Views: 9

Authors

H. Benjamin Fredrick David
Department of Computer Science and Engineering, Manonmaniam Sundaranar University, India
A. Suruliandi
Department of Computer Science and Engineering, Manonmaniam Sundaranar University, India

Abstract


Data Mining is the procedure which includes evaluating and examining large pre-existing databases in order to generate new information which may be essential to the organization. The extraction of new information is predicted using the existing datasets. Many approaches for analysis and prediction in data mining had been performed. But, many few efforts has made in the criminology field. Many few have taken efforts for comparing the information all these approaches produce. The police stations and other similar criminal justice agencies hold many large databases of information which can be used to predict or analyze the criminal movements and criminal activity involvement in the society. The criminals can also be predicted based on the crime data. The main aim of this work is to perform a survey on the supervised learning and unsupervised learning techniques that has been applied towards criminal identification. This paper presents the survey on the Crime analysis and crime prediction using several Data Mining techniques.

Keywords


Criminology, Crime Analysis, Crime Prediction, Data Mining.

References