Contextual Visualization of Crime Matching Through Interactive Clustering and Bayesian Theory
- 151 Downloads
Police and law enforcement agencies perform social media analysis to gain a better understanding of criminal social networks structures and to identify potential criminal activities. The use of data mining techniques in social media analysis, however, faces issues and challenges such as linkage-based structural analysis, association extraction, community or group detection, behaviour and mood analysis, sentiment analysis and dynamic analysis of streaming networks. This chapter describes the extension of our developed framework and propose an association model for extracting multilevel associations based on associative questioning. We also describe data mining techniques used to visualize these associations through a 2D crime cluster space. The developed framework provides a complete data analytic solution towards identifying and understanding associations between crime entities and thus expedites the crime matching process.
KeywordsCrime matching Crime clustering Criminal analysis Social media analysis Data mining Associative questioning Methodology
The research leading to the results reported here has received funding from the European Union Seventh Framework Programme through Project VALCRI, European Commission Grant Agreement N FP7-IP-608142, awarded to Middlesex University and partners.
- Alruily, M., Ayesh, A., & Al-Marghilani, A. (2010). Using self organizing map to cluster Arabic crime documents. In Computer science and information technology (IMCSIT), proceedings of the 2010 International Multiconference on. IEEE, pp. 357–363.Google Scholar
- Brown, D. E. (1998). The regional crime analysis program (recap): A framework for mining data to catch criminals. In Systems, Man, and Cybernetics, 1998 IEEE International Conference on, vol. 3. IEEE, 1998, pp. 2848–2853.Google Scholar
- Bsoul, Q., Salim, J., & Zakaria, L. Q. (2013). An intelligent document clustering approach to detect crime patterns. Procedia Technology, 11, 1181–1187, 2013, 4th International Conference on Electrical Engineering and Informatics, ICEEI 2013Google Scholar
- Didimo, W., Liotta, G., Montecchiani, F., & Palladino, P. (2011). An advanced network visualization system for financial crime detection. In 2011 IEEE Pacific visualization symposium, pp. 203–210.Google Scholar
- Ding, L., Steil, D., Hudnall, M., Dixon, B., Smith, R., Brown, D., & Parrish, A. (2009). Perpsearch: An integrated crime detection system. In Proceedings of the 2009 IEEE international conference on intelligence and security informatics, ser. ISI’09. Piscataway, NJ: IEEE Press, 2009, pp. 161–163Google Scholar
- Isah, H., Neagu, D., & Trundle, P. (2015). Bipartite network model for inferring hidden ties in crime data. In 2015 IEEE/ACM international conference on advances in social networks analysis and mining (ASONAM), pp. 994–1001.Google Scholar
- Le-Khac, N., Markos, S., & Kechadi, M. T. (2016). A data mining-based solution for detecting suspicious money laundering cases in an investment bank. CoRR, vol. abs/1609.00990, 2016.Google Scholar
- Nath, S. V. (2006). Crime pattern detection using data mining. In Web intelligence and intelligent agent technology workshops, 2006. WI-IAT 2006 workshops. 2006 IEEE/WIC/ACM International Conference on. IEEE, pp. 41–44.Google Scholar
- Park, A. J., Tsang, H. H., & Brantingham, P. L. (2012). Dynalink: A framework for dynamic criminal network visualization. In 2012 European intelligence and security informatics conference, pp. 217–224.Google Scholar
- Qazi, N., & Wong, B. L. W. (2017). Behavioural tempo-spatial knowledge graph for crime matching through graph theory. In: 2017 European Intelligence and Security Informatics Conference (EISIC), pp 143–146, https://doi.org/10.1109/EISIC.2017.29.
- Qazi, N., Wong, B. L. W., Kodagoda, N., & Adderley, R. (2016). Associative search through formal concept analysis in criminal intelligence analysis. In 2016 IEEE international conference on systems, Man, and Cybernetics (SMC), pp. 001 917–001 922.Google Scholar
- Rasheed, A., & Wiil, U. K. (2015). A tool for analysis and visualization of criminal networks. In: 2015 17th UKSim-AMSS International Conference on Modelling and Simulation (UKSim), pp 97–102Google Scholar
- Takuya, W., & Masuhara, H. (2011). A spontaneous code recommendation tool based on associative search. In: Proceedings of the 3rd International Workshop on search-driven development: Users, infrastructure, tools, and evaluation, ACM, New York, NY, USA, SUITE ‘11, pp 17–20, https://doi.org/10.1145/1985429.1985434.
- Thongsatapornwatana, U., & Chuenmanus, C. (2014). Suspect vehicle detection using vehicle reputation with association analysis concept. In 2014 IIAI 3rd International Conference on Advanced Applied Informatics. pp. 436–440Google Scholar