Big Data, Data Mining, Machine Learning, and Deep Learning Concepts in Crime Data

Abstract

Along with the rapid change of information technologies and the widespread use of the internet over time, data stacks with ample diversity and quite large volumes has emerged. The use of data mining is increasing day by day due to the huge part it plays in the acquisition of information by making necessary analyses especially within a large amount of data. Obtaining accurate information is a key factor affecting decision-making processes. Crime data is included among the application areas of data mining, being one of the data stacks which is rapidly growing with each passing day. Crime events constitute unwanted behaviour in every society. For this reason, it is important to extract meaningful information from crime data. This article aims to provide an overview of the use of data mining and machine learning in crime data and to give a new perspective on the decision-making processes by presenting examples of the use of data mining for a crime. For this purpose, some examples of data mining and machine learning in crime and security areas are presented by giving a conceptual framework in the subject of big data, data mining, machine learning, and deep learning along with task types, processes, and methods.

Keywords

• Crime
• Big data
• Data mining
• Security
• Policing

References

1. Abdullah, N., Ismail, S. A., Sophiayati, S., & Sam, S. M. (2015). Data quality in big data: a review. International Journal of Advances in Soft Computing & Its Applications, 7(3). google scholar
2. Adamović, S., Miškovic, V., Maček, N., Milosavljević, M., Šarac, M., Saračević, M., & Gnjatović, M. (2020). An efficient novel approach for iris recognition based on stylometric features and machine learning techniques. Future Generation Computer Systems, 107, 144-157. google scholar
3. Adewumi, A. O., & Akinyelu, A. A. (2017). A survey of machine-learning and nature-inspired based credit card fraud detection techniques. International Journal of System Assurance Engineering and Management, 8(2), 937-953. google scholar
4. Aggarwal, C. C. (2018). Machine learning for text. Cham: Springer International Publishing. google scholar
5. Agrahari, A., & Rao, D. (2017). A review paper on Big Data: technologies, tools and trends. Int Res J Eng Technol, 4(10), 640-649. google scholar
6. Agu, S. C., Ajah, I., & Ibe, W. E. (2019). Impact of Human Character and Information System on Corruption Risk in Nigeria. International Journal of Scientific Research and Engineering Development, 2(4), 481-485. google scholar
7. Ahmed, A. (2020). “From Data to Wisdom” Using Machine Learning Capabilities in Accounting and Finance Professionals. Talent Development & Excellence, 12. google scholar
8. Ajans Press, 2017. [Online]. Available: https://www.cnnturk.com/bilim-teknoloji/ turkiye-cep-telefonuylakonusmada-avrupa-birincisi?page=1 (accessed 5.12.18). google scholar

9. Akdemir, N., & Lawless, C. J. (2020). Exploring the human factor in cyber-enabled and cyber-dependent crime victimisation: a lifestyle routine activities approach. Internet Research, 30(6), 1665-1687. google scholar
10. Aledhari, M., Di Pierro, M., Hefeida, M., & Saeed, F. (2018). A deep learning-based data minimization algorithm for fast and secure transfer of big genomic datasets. IEEE Transactions on Big Data. google scholar
11. Arora, S., Bhatia, M. P. S., & Kukreja, H. (2020, February). A Multimodal Biometric System for Secure User Identification Based on Deep Learning. In International Congress on Information and Communication Technology (pp. 95-103). Springer, Singapore. google scholar
12. Arshad, H., Jantan, A., & Omolara, E. (2019). Evidence collection and forensics on social networks: Research challenges and directions. Digital Investigation, 28, 126-138. google scholar
13. Ateş, E.C., Bostanci, E., & Guzel, M. S. (2020). Security Evaluation of Industry 4.0: Understanding Industry 4.0 on the Basis of Crime, Big Data, Internet Of Thing (IoT) and Cyber Physical Systems. Güvenlik Bilimleri Dergisi, (International Security Congress Special Issue), 29-50. google scholar
14. Ayre, L. B., & Craner, J. (2017). Open data: What it is and why you should care. Public Library Quarterly, 36(2), 173-184. google scholar
15. Beniwal, S., & Arora, J. (2012). Classification and feature selection techniques in data mining. International journal of engineering research & technology (IJERT), 1(6), 1-6. google scholar
16. Berk, R. (2017). An impact assessment of machine learning risk forecasts on parole board decisions and recidivism. Journal of Experimental Criminology, 13(2), 193-216. google scholar
17. Berkhin, P. (2006). A survey of clustering data mining techniques. In Grouping multidimensional data (pp. 25- 71). Springer, Berlin, Heidelberg. google scholar
18. Bhuyan, M. H., Saharia, S., & Bhattacharyya, D. K. (2012). An effective method for fingerprint classification. arXiv preprint arXiv:1211.4658. google scholar
19. Blei, D. M., & Smyth, P. (2017). Science and data science. Proceedings of the National Academy of Sciences, 114(33), 8689-8692. google scholar
20. Bock, F. E., Aydin, R. C., Cyron, C. J., Huber, N., Kalidindi, S. R., & Klusemann, B. (2019). A review of the application of machine learning and data mining approaches in continuum materials mechanics. Frontiers in Materials, 6, 110. google scholar
21. Bode, J. (2019, June). Every Contact Leaves a Trace: A Literary Reality of Locard’s Exchange Principle. In Outside the Box: A Multi-Lingual Forum (p. 18). google scholar
22. Bostanci, E. (2015). 3D reconstruction of crime scenes and design considerations for an interactive investigation tool. arXiv preprint arXiv:1512.03156. google scholar
23. Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, communication & society, 15(5), 662-679. google scholar
24. Bulgakova, E., Bulgakov, V., Trushchenkov, I., Vasilev, D., & Kravets, E. (2019). Big data in investigating and preventing crimes. In Big Data-driven World: Legislation Issues and Control Technologies (pp. 61-69). Springer, Cham. google scholar
25. Campbell, C., & Ying, Y. (2011). Learning with support vector machines. Synthesis lectures on artificial intelligence and machine learning, 5(1), 1-95. google scholar
26. Ch, R., Gadekallu, T. R., Abidi, M. H., & Al-Ahmari, A. (2020). Computational System to Classify Cyber Crime Offenses Using Machine Learning. Sustainability, 12(10), 4087. google scholar
27. Chan, J., & Bennett Moses, L. (2017). Making sense of big data for security. The British journal of criminology, 57(2), 299-319. google scholar
28. Chau, D. H., Pandit, S., & Faloutsos, C. (2006, September). Detecting fraudulent personalities in networks of online auctioneers. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 103-114). Springer, Berlin, Heidelberg. google scholar
29. Chen, M., Mao, S., & Liu, Y. (2014). Big data: A survey. Mobile networks and applications, 19(2), 171-209. google scholar
30. Clarke, C. (2006). Proactive policing: Standing on the shoulders of community‐based policing. Police Practice and Research, 7(1), 3-17. google scholar
31. Commission, (2017). Kriminalistik. Gendarmerie and Coast Guard Academy, Ankara. google scholar
32. Cooper, P. (2017). Data, information, knowledge and wisdom. Anaesthesia & Intensive Care Medicine, 18(1), 55-56. google scholar
33. Dey, A. (2016). Machine learning algorithms: a review. International Journal of Computer Science and Information Technologies, 7(3), 1174-1179. google scholar
34. Feng, M., Zheng, J., Ren, J., Hussain, A., Li, X., Xi, Y., & Liu, Q. (2019). Big data analytics and mining for effective visualization and trends forecasting of crime data. IEEE Access, 7, 106111-106123. google scholar
35. Ge, Z., Song, Z., Ding, S. X., & Huang, B. (2017). Data mining and analytics in the process industry: The role of machine learning. Special Section On Data-Driven Monitoring, Fault Diagnosis and Control Of CyberPhysical Systems, 5, 20590-20616. google scholar
36. Ghorbani, R., & Ghousi, R. (2019). Predictive data mining approaches in medical diagnosis: A review of some diseases prediction. International Journal of Data and Network Science, 3(2), 47-70. google scholar
37. Guenther, A.J. (2012). Role of Social Media in Law Enforcement Significant and Growing [Online]. Available: http://www.lexisnexis.com/en-us/about-us/media/press-release.page?id =1342623085481181 google scholar
38. Gupta, M. K., & Chandra, P. (2019, March). A comparative study of clustering algorithms. In 2019 6th International Conference on Computing for Sustainable Global Development (INDIACom) (pp. 801-805). IEEE. google scholar
39. Hand, D. J., & Adams, N. M. (2014). Data Mining. Wiley StatsRef: Statistics Reference Online, 1-7. google scholar
40. 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. google scholar
41. He, L., Páez, A., Jiao, J., An, P., Lu, C., Mao, W., & Long, D. (2020). Ambient Population and Larceny-Theft: A Spatial Analysis Using Mobile Phone Data. ISPRS International Journal of Geo-Information, 9(6), 342. google scholar
42. Heickerö, R. (2014). Cyber terrorism: Electronic jihad. Strategic Analysis, 38(4), 554-565. google scholar
43. Hey, J. (2004). The data, information, knowledge, wisdom chain: the metaphorical link. Intergovernmental Oceanographic Commission, 26, 1-18. google scholar
44. Jackson, J. (2002). Data mining; a conceptual overview. Communications of the Association for Information Systems, 8(1), 19. google scholar
45. Kelleher, J. D., & Tierney, B. (2018). Data science. MIT Press. google scholar
46. Khare, A. R., & Shrivasta, P. (2018). Data mining for the internet of things. In Exploring the Convergence of Big Data and the Internet of Things (pp. 181-191). IGI Global. google scholar
47. Koyuncugil, A. S., & Özgülbaş, N. (2009). Veri madenciliği: Tıp ve sağlık hizmetlerinde kullanımı ve uygulamaları. İnternational Journal Of Informatics Technologies, 2(2). google scholar
48. Kumar, R., & Nagpal, B. (2019). Analysis and prediction of crime patterns using big data. International Journal of Information Technology, 11(4), 799-805. google scholar
49. Lau, P. Y., & Fung, W. K. (2020). Evaluation of marker selection methods and statistical models for chronological age prediction based on DNA methylation. Legal Medicine, 47, 101744. google scholar
50. Lei, C. (2019). Legal Control over Big Data Criminal Investigation. Social Sciences in China, 40(3), 189-204. google scholar
51. Li, X., Liu, B., & Philip, S. Y. (2006, September). Discovering overlapping communities of named entities. In European Conference on Principles of Data Mining and Knowledge Discovery (pp. 593-600). Springer, Berlin, Heidelberg. google scholar
52. Martinovic, I., Rasmussen, K., Roeschlin, M., & Tsudik, G. (2017). Authentication using pulse-response biometrics. Communications of the ACM, 60(2), 108-115. google scholar
53. Mcclendon, L., & Meghanathan, N. (2015). Using machine learning algorithms to analyze crime data. Machine Learning and Applications: An International Journal (MLAIJ), 2(1), 1-12. google scholar
54. McCue, C. (2014). Data mining and predictive analysis: Intelligence gathering and crime analysis. ButterworthHeinemann. google scholar
55. Mesgarpour, M., & Dickinson, I. (2014). Enhancing the value of commercial vehicle telematics data through analytics and optimisation techniques. Archives of Transport System Telematics, 7. google scholar
56. Mistek, E., Fikiet, M. A., Khandasammy, S. R., & Lednev, I. K. (2018). Toward locard’s exchange principle: recent developments in forensic trace evidence analysis. Analytical chemistry, 91(1), 637-654. google scholar
57. Mittal, M., Goyal, L. M., Hemanth, D. J., & Sethi, J. K. (2019). Clustering approaches for high‐dimensional databases: A review. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery, 9(3), e1300. google scholar
58. Mittal, M., Goyal, L. M., Sethi, J. K., & Hemanth, D. J. (2018). Monitoring the impact of economic crisis on crime in India using machine learning. Computational Economics, 53(4), 1467-1485. google scholar
59. Mukhopadhyay, A., Maulik, U., Bandyopadhyay, S., & Coello, C. A. C. (2013). A survey of multiobjective evolutionary algorithms for data mining: Part I. IEEE Transactions on Evolutionary Computation, 18(1), 4-19. google scholar
60. Muneer, A., & Fati, S. M. (2020). A Comparative Analysis of Machine Learning Techniques for Cyberbullying Detection on Twitter. Future Internet, 12(11), 187. google scholar
61. Ngai, E. W., Xiu, L., & Chau, D. C. (2009). Application of data mining techniques in customer relationship management: A literature review and classification. Expert systems with applications, 36(2), 2592-2602. google scholar
62. Odia, J. O., & Akpata, O. T. (2020). Role of Data Science and Data Analytics in Forensic Accounting and Fraud Detection. In Handbook of Research on Engineering, Business, and Healthcare Applications of Data Science and Analytics (pp. 203-227). IGI Global. google scholar
63. Olson, D. L., & Lauhoff, G. (2019). Descriptive data mining. In Descriptive Data Mining (pp. 129-130). Springer, Singapore. google scholar
64. Pandey R.K., Zhou Y., Kota B.U., Govindaraju V. (2017) Learning Representations for Cryptographic Hash Based Face Template Protection. In: Bhanu B., Kumar A. (eds) Deep Learning for Biometrics. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-61657-5_11 google scholar
65. Pandya, B., Cosma, G., Alani, A. A., Taherkhani, A., Bharadi, V., & McGinnity, T. M. (2018, May). Fingerprint classification using a deep convolutional neural network. In 2018 4th International Conference on Information Management (ICIM) (pp. 86-91). IEEE. google scholar
66. Pauleen, D. J., Rooney, D., & Intezari, A. (2017). Big data, little wisdom: trouble brewing? Ethical implications for the information systems discipline. Social Epistemology, 31(4), 400-416. google scholar
67. Perols, J. (2011). Financial statement fraud detection: An analysis of statistical and machine learning algorithms. Auditing: A Journal of Practice & Theory, 30(2), 19-50. google scholar
68. Power, D. J. (2016). “Big Brother” can watch us. Journal of Decision systems, 25(sup1), 578-588. google scholar
69. Quick, D., & Choo, K. K. R. (2016). Big forensic data reduction: digital forensic images and electronic evidence. Cluster Computing, 19(2), 723-740. google scholar
70. Ristea, A., Al Boni, M., Resch, B., Gerber, M. S., & Leitner, M. (2020). Spatial crime distribution and prediction for sporting events using social media. International Journal of Geographical Information Science, 1-32. google scholar
71. Roy, A., Sun, J., Mahoney, R., Alonzi, L., Adams, S., & Beling, P. (2018, April). Deep learning detecting fraud in credit card transactions. In 2018 Systems and Information Engineering Design Symposium (SIEDS) (pp. 129-134). IEEE. google scholar
72. Rutkowski, L., Jaworski, M., & Duda, P. (2020). Stream data mining: algorithms and their probabilistic properties. Cham: Springer. google scholar
73. Shao, L., Duan, Y., Sun, X., Gao, H., Zhu, D., & Miao, W. (2017, July). Answering Who/When, What, How, Why through Constructing Data Graph, Information Graph, Knowledge Graph and Wisdom Graph. In SEKE (pp. 1-6). google scholar
74. Snaphaan, T., & Hardyns, W. (2019). Environmental criminology in the big data era. European Journal of Criminology, 1477370819877753. google scholar
75. Song, G., Bernasco, W., Liu, L., Xiao, L., Zhou, S., & Liao, W. (2019). Crime feeds on legal activities: Daily mobility flows help to explain thieves’ target location choices. Journal of Quantitative Criminology, 35(4), 831-854. google scholar
76. Srinivas, K., Rani, B. K., & Govrdhan, A. (2010). Applications of data mining techniques in healthcare and prediction of heart attacks. International Journal on Computer Science and Engineering (IJCSE), 2(02), 250-255. google scholar
77. Steenbruggen, J., Tranos, E., & Nijkamp, P. (2015). Data from mobile phone operators: A tool for smarter cities?. Telecommunications Policy, 39(3-4), 335-346. google scholar
78. Stewart, L. (2019). Big Data Discrimination: Maintaining Protection of Individual Privacy without Disincentivizing Businesses’ Use of Biometric Data to Enhance Security. BCL Rev., 60, 349. google scholar
79. Sundararajan, K., & Woodard, D.L. (2018). Deep Learning for Biometrics: A Survey. ACM Comput. Surv. 51(3), DOI:https://doi.org/10.1145/3190618. google scholar
80. Tassone, C., Martini, B., & Choo, K. K. (2017). Forensic visualization: survey and future research directions. In Contemporary Digital Forensic Investigations of Cloud and Mobile Applications (pp. 163-184). Syngress. google scholar
81. Tilley, N., & Sidebottom, A. (2017). Handbook of crime prevention and community safety. Routledge. google scholar
82. Tirgari, V. (2012). Information technology policies and procedures against unstructured data: A phenomenological study of information technology professionals. Journal of Management Information and Decision Sciences, 15(2), 87. google scholar
83. Tiwari, S., Chourasia, J. N., & Chourasia, V. S. (2015). A review of advancements in biometric systems. International Journal of Innovative Research in Advanced Engineering, 2(1), 187-204. google scholar
84. Traunmueller, M., Quattrone, G., & Capra, L. (2014, November). Mining mobile phone data to investigate urban crime theories at scale. In International Conference on Social Informatics (pp. 396-411). Springer, Cham. google scholar
85. Turkish Ministry of Justice. (2020). Judicial Statistics 2019. [Online]. Available: https://adlisicil.adalet.gov.tr/ Resimler/SayfaDokuman/1092020162733adalet_ist-2019.pdf (accessed 10.09.20). google scholar
86. Turing, A., (1950). Computing machinery and intelligence: Mind, 59, 433–460. google scholar
87. TÜİK (Turkish Statistical Institute). (2020). Adrese Dayalı Nüfus Kayıt Sistemi Sonuçları, 2019. [Online]. Available: https://adlisicil.adalet.gov.tr/Resimler/SayfaDokuman /1092020162733adalet_ist-2019.pdf (accessed 10.09.20). google scholar
88. Umair, S., Muhammad, S., Amna, U., Aniqa, M., Abdul, B.S., Sheikh, K.R., (2015). Application of Machine learning Algorithms in Crime Classification and Classification Rule Mining. Res. J. Recent Sci. (pp. 106–114). google scholar
89. Uliyan, D. M., Sadeghi, S., & Jalab, H. A. (2020). Anti-spoofing method for fingerprint recognition using patch based deep learning machine. Engineering Science and Technology, an International Journal, 23(2), 264-273. google scholar
90. Vaidhyanathan, S., & Bulock, C. (2014). Knowledge and dignity in the era of “big data”. The Serials Librarian, 66(1-4), 49-64. google scholar
91. Wamba, S. F., Akter, S., Edwards, A., Chopin, G., & Gnanzou, D. (2015). How ‘big data’can make big impact: Findings from a systematic review and a longitudinal case study. International Journal of Production Economics, 165, 234-246. google scholar
92. Wang, H., Kifer, D., Graif, C., & Li, Z. (2016, August). Crime rate inference with big data. In Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining (pp. 635-644). google scholar Wani, M. A., Bhat, F. A., Afzal, S., & Khan, A. I. (2020). Supervised Deep Learning in Fingerprint Recognition. In Advances in Deep Learning (pp. 111-132). Springer, Singapore. google scholar Wearesocials & Hootsuide, (2018). Digital in 2018: World’s ınternet users pass the 4 billion mark. URL https:// wearesocial.com/blog/2018/01/global-digital-report-2018 (accessed 16.02.20). google scholar Wheeler, A. P., & Steenbeek, W. (2020). Mapping the risk terrain for crime using machine learning. Journal of Quantitative Criminology, 1-36. google scholar
93. White, M. (2012). Digital workplaces: Vision and reality. Business information review, 29(4), 205-214. google scholar
94. Williams, G. J. (2009). Rattle: a data mining GUI for R. The R Journal, 1(2), 45-55. google scholar
95. Williams, M. L., Burnap, P., Javed, A., Liu, H., & Ozalp, S. (2020). Hate in the machine: anti-Black and antiMuslim social media posts as predictors of offline racially and religiously aggravated crime. The British Journal of Criminology, 60(1), 93-117. google scholar
96. Wilson, D. B., McClure, D., & Weisburd, D. (2010). Does forensic DNA help to solve crime? The benefit of sophisticated answers to naive questions. Journal of Contemporary Criminal Justice, 26(4), 458-469. google scholar
97. Win, K. N., Li, K., Chen, J., Viger, P. F., & Li, K. (2020). Fingerprint classification and identification algorithms for criminal investigation: A survey. Future Generation Computer Systems, 110, 758-771. google scholar
98. Xu, H. (2020). Big data challenges in genomics. In Handbook of Statistics (Vol. 43, pp. 337-348). Elsevier google scholar
99. Yao, S., Wei, M., Yan, L., Wang, C., Dong, X., Liu, F., & Xiong, Y. (2020, August). Prediction of Crime Hotspots based on Spatial Factors of Random Forest. In 2020 15th International Conference on Computer Science & Education (ICCSE) (pp. 811-815). IEEE. google scholar
100. Yavanoglu, U., Colak, M., Caglar, B., Cakir, S., Milletsever, O., & Sagiroglu, S. (2013, December). Intelligent approach for identifying political views over social networks. In 2013 12th International Conference on Machine Learning and Applications (Vol. 2, pp. 281-287). IEEE. google scholar
101. Yoo, J. S. (2019, December). Crime data warehousing and crime pattern discovery. In Proceedings of the Second International Conference on Data Science, E-Learning and Information Systems (pp. 1-6). google scholar
102. Zins, C. (2007). Conceptual approaches for defining data, information, and knowledge. Journal of the American society for information science and technology, 58(4), 479-493. google scholar