A survey of big data and data mining techniques for crime prevention
Keywords:
Big Data, Crimes, Data Mining, SurveyAbstract
Crimes remain a serious challenge to many societies and nations across the globe, despite technological advancement. Efforts by security agencies must remain a step ahead of potential attacks in order to effectively prevent crimes from occurring. The police stations and other related criminal justice departments have several large databases that can be used to forecast or examine criminal activities. Data Mining, the method of uncovering sensitive information from big data, is now an effective tool for combating, curbing and preventing crimes of all sorts. The main aim of this paper is to provide a systematic survey of how Data Mining techniques are applicable in combating crimes. In this paper, we selected widely applicable data mining techniques that were commonly used for crime analysis and prevention in previous related studies. A summary table of many relevant crime- Data Mining applications is included that will serve as a reference for researchers. Each technique has its specific use. Data mining methods are quickly explained to the reader that includes Classification, Cluster analysis, Association rue mining, Entity extraction and Social network analysis amongst others.
References
Adderley, R., & Musgrove, P. B. (2001, August). Data mining case study: Modeling the behavior of offenders who commit serious sexual assaults. In Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 215-220).
Hassani, H., Huang, X., Silva, E. S., & Ghodsi, M. (2016). A review of data mining applications in crime. Statistical Analysis and Data Mining: The ASA Data Science Journal, 9(3), 139-154.
Anuar, S., Selamat, A., &Sallehuddin, R. (2015). Hybrid Particle Swarm Optimization Feature Selectionfor Crime Classification Hybrid Particle Swarm Optimization Feature Selection for CrimeClassification. (January). https://doi.org/10.1007/978- 3-319-16211-9
Balamurugan, S. A., & Pandian, M. (2007). Association Rule Mining for Suspicious Email Detection : A Data Mining Approach Exformampleofeclassifying.
Balasupramanian, N., Ephrem, B. G., & Al-barwani, I. S. (2017). User Pattern Based Online Fraud Detection and Prevention using Big Data Analytics and Self Organizing Maps. 691–694.
Bharathi, A., & Shilpa, R. (2014). A survey on crime data analysis of data mining using clustering techniques. International Journal of Advance Research in Computer Science and Management Studies, 2(8), 9-13.
Bhowmik, R. (2011). Detecting auto insurance fraud by data mining techniques. Journal of Emerging Trends in Computing and Information Sciences, 2(4), 156-162.
Chen, H., Chung, W., Qin, Y., Chau, M., Xu, J. J., Wang, G., & Zheng, R. (2002). Crime Data Mining : An Overview and Case Studies.
Grubesic, T. H., & Murray, A. T. (2001). Detecting Hot Spots Using Cluster Analysis and GIS. Detecting Hot Spots Using Cluster Analysis and GIS.
Gupta, M., Chandra, B., & Gupta, M. P. (2008). Crime data mining for Indian police information system. Computer society of India, 40(1), 388-397.
Hu, J. (2019). Big Data Analysis of Criminal Investigations. 2018 5th International Conference on Systems and Informatics, ICSAI 2018, (Icsai), 649–654. https://doi.org/10.1109/ICSAI.2018.8599305
Hui, W., Jing, W., & Tao, Z. (2011, March). Analysis of decision tree classification algorithm based on attribute reduction and application in criminal behavior. In 2011 3rd International Conference on Computer Research and Development (Vol. 1, pp. 27-30). IEEE.
Brown, D. E., & Hagen, S. (2003). Data association methods with applications to law enforcement. Decision Support Systems, 34(4), 369-378
Fayyad, U., & Uthurusamy, R. (Eds.). (2002). Evolving data into mining solutions for insights. Communications of the ACM, 45(8), 28-31.
Iqbal, R., Azrifah, M., Murad, A., & Mustapha, A. (2013). An Experimental Study of Classification Algorithms for Crime Prediction. 6.
Jayaweera, I., Sajeewa, C., Wijewardane, T., &Perera, I. (2015). Crime Analytics : Analysis of Crimes Through Newspaper Articles.
Jennifer, J., Chau, M., Xu, J. J., & Chen, H. (2002). Extracting Meaningful Entities from Police Narrative Reports Extracting Meaningful Entities from Police Narrative Reports.
Kaur, N. (2016). Data Mining Techniques used in Crime Analysis : - A Review. 1981–1984.
Kolajo, T., & Daramola, O. (2017). Leveraging Big Data to Combat Terrorism in Developing Countries. (March). https://doi.org/10.1109/ICTAS.2017.7920662
Kumar, A. S. (2015). Data Mining Based Crime Investigation Systems : Taxonomy and Relevance. (Gcct), 850–853.
Kumar, D., & Arti, T. (2015). Crime detection and criminal identification in India using data mining techniques. 117–127. https://doi.org/10.1007/s00146-014-0539-6
Matto, G., &Mwangoka, J. (2017). Detecting crime patterns from Swahili newspapers using text mining. 4(2), 145–156.
Nath, S. V. (2006). Crime Pattern Detection Using Data Mining. 338–341.
Ng, V., Chan, S., Lau, D., & Ying, C. M. (2007). Incremental Mining for Temporal Association Rules for Crime Pattern Discoveries. 63(c).
Nwanga, M. E., Okafor, K. C., Onwuka, E. N., &Nosiri, O. C. (2017). Application of Complex Network Analysis Theories.
Okonkwo, R. O., &Enem, F. O. (2011). Combating Crime and Terrorism Using Data Mining Techniques. Information Technology for People-Centred Development ( ITePED 2011 ), (ITePED).
Police, I., Symposium, E., Centre, G., The, F. O. R., Control, D., Forces, O. F. A., & Safety, C. (2011). Social network analysis in an operational environment : Defining the utility of a network approach for crime analysis using the Richmond CityPolice Department as a case study. (November).
Pramanik, M. I., Lau, R. Y. K., Yue, W. T., Ye, Y., & Li, C. (2017). Big data analytics for security and criminal investigations. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 7(4), 1–19. https://doi.org/10.1002/widm.1208
Rani, A., &Rajasree, S. (2018). Crime Trend Analysis and Prediction Using Mahanolobis Distance and Dynamic Time Warping Technique. (September), 1–6. https://doi.org/10.13140/RG.2.2.26605.23525
Chai, R. M., & Wang, M. (2010, April). A more efficient classification scheme for ID3. In 2010 2nd International Conference on Computer Engineering and Technology (Vol. 1, pp. V1-329). IEEE.
Sakhare, N., & Joshi, S. (2014). Criminal Identification System Based on Data Mining. 3rd ICRTET, ISBN, (978-93), 5107-220.
Sathyadevan, S., & Devan, M. S. (2014). Crime Analysis and Prediction Using Data Mining. 406–412.
Sharma, M. (2014). Z - CRIME : A Data Mining Tool for the Detection of Suspicious Criminal Activities Based on Decision Tree.
Sivaraman, R., Srinivasan, S., &Chandrasekeran, R. M. (2015). Big Data On Terrorist Attacks : An Analysis Using The Ensemble Classifier Approach. 255–261.
Taha, K., Member, S., Yoo, P. D., & Member, S. (2013). SIIMCO : A Forensic Investigation Tool for Identifying the Influential Members of a Criminal Organization.
Tao, S. W., Yang, O. C., Salim, M. S. B. M., & Husain, W. (2018). A proposed Bi-layer crime prevention framework using big data analytics.
International Journal on Advanced Science, Engineering and Information Technology, 8(4–2), 1453–1459. https://doi.org/10.18517/ijaseit.8.4-2.6802
Thongtae, P., &Srisuk, S. (2008). An Analysis of Data Mining Applications in Crime Domain. 122–126. https://doi.org/10.1109/CIT.2008.Workshops.80
Toure, I., &Gangopadhyay, A. (2013, November). Analyzing terror attacks using latent semantic indexing. In 2013 IEEE International Conference on Technologies for Homeland Security (HST) (pp. 334-337). IEEE.
Wang, C., & Liu, P. (2008). Data Mining and Hotspot Detection in an Urban Development Project. 6, 389–414.
Yu, C., Ward, M. W., Morabito, M., & Ding, W. (2011). Crime Forecasting Using Data Mining Techniques.
Zhang, X., Hu, Z., Li, R., & Zheng, Z. (2008). Detecting and Mapping Crime Hot Spots Based on Improved Attribute Oriented Induce Clustering. (1).
Zubi, Z. S., &Mahmmud, A. A. (2013). Using Data Mining Techniques to Analyze Crime patterns in the Libyan National Crime Data 2 Why Analyze Crime 5 Data Mining Task. 79–85.
Chai, R. M., & Wang, M. (2010, April). A more efficient classification scheme for ID3. In 2010 2nd International Conference on Computer Engineering and Technology (Vol. 1, pp. V1-329). IEEE.
Huang, A. H., & Chen, X. T. (2009). An improved ID3 algorithm of decision trees. Computer Engineering and Science, 31(6), 109-111.
Ngoge, L., & Orero, J. O. (2017). Mapping of terrorist activities in Kenya using sentiment analysis.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 COMPUSOFT: An International Journal of Advanced Computer Technology
This work is licensed under a Creative Commons Attribution 4.0 International License.
©2023. COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY by COMPUSOFT PUBLICATION is licensed under a Creative Commons Attribution 4.0 International License. Based on a work at COMPUSOFT: AN INTERNATIONAL OF ADVANCED COMPUTER TECHNOLOGY. Permissions beyond the scope of this license may be available at Creative Commons Attribution 4.0 International Public License.