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Investigating Crimes using Text Mining and Network Analysis
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 126 - Number 8 |
| Year of Publication: 2015 |
| Authors: Nael T. Elyezjy, Alaa M. Elhaless |
10.5120/ijca2015906134
|
Nael T. Elyezjy, Alaa M. Elhaless . Investigating Crimes using Text Mining and Network Analysis. International Journal of Computer Applications. 126, 8 ( September 2015), 19-25. DOI=10.5120/ijca2015906134
Abstract
In these days, security of citizens is considered one of the major concerns of any government in the world. In every country, there is a huge amount of unstructured texts coming from investigating offenders in police departments. As a result, the importance of crime analysis is growing day after day. There is a little research; in methods and techniques that extract criminal networks from unstructured investigations texts especially in Arabic language. In our proposed system, we climb three main distinct contributions to discover forensics using investigation documents. The first by extracting offender names from unstructured text. Secondly, by constructing a crime network from real Arabic investigation documents. Finally, we provide analysis of the interaction between offenders in different documents that directly and indirectly related used to discover a new clue used to solve the crime puzzle. To evaluate the performance and effectiveness of the proposed system, real unstructured documents about investigations are obtained from police departments in the Gaza Strip. The experimental results show that the proposed system is effective in identifying proper offender person's name from real Arabic Documents. The average results for our system using the F-measure is 89% also the average of F-measure in a proposed algorithm for discovery hidden relationship arrive to 92%. In addition; we found that our approach achieves best F-measure results in most cases.
References
- M. Alruily, A. Ayesh, and H. Zedan, "Crime type document classification from arabic corpus," in Developments in eSystems Engineering (DESE), 2009 Second International Conference on, 2009, pp. 153-159.
- M. Alruily, A. Ayesh, and A. Al-Marghilani, "Using Self Organizing Map to Cluster Arabic Crime Documents," in Computer Science and Information Technology (IMCSIT), Proceedings of the 2010 International Multiconference on, 2010, pp. 357-363.
- A. ALKAFF and M. MOHD, "EXTRACTION OF NATIONALITY FROM CRIME NEWS," Journal of Theoretical & Applied Information Technology, vol. 53, 2013.
- H. Chen, J. Schroeder, R. V. Hauck, L. Ridgeway, H. Atabakhsh, H. Gupta, et al., "COPLINK Connect: information and knowledge management for law enforcement," Decision Support Systems, vol. 34, pp. 271-285, 2003.
- K. Baumgartner, S. Ferrari, and G. Palermo, "Constructing Bayesian networks for criminal profiling from limited data," Knowledge-Based Systems, vol. 21, pp. 563-572, 2008.
- R. Al-Zaidy, B. Fung, A. M. Youssef, and F. Fortin, "Mining criminal networks from unstructured text documents," Digital Investigation, vol. 8, pp. 147-160, 2012.
- D. Prakash and S. Surendran, "Detection and Analysis of Hidden Activities in Social Networks," International Journal of Computer Applications, vol. 77, pp. 34-38, 2013.
- R. Al-Zaidy, B. C. Fung, A. M. Youssef, and F. Fortin, "Mining criminal networks from unstructured text documents," Digital Investigation, vol. 8, pp. 147-160, 2012.
- R. Alfred, L. C. Leong, C. K. On, and P. Anthony, "Malay Named Entity Recognition Based on Rule-Based Approach," International Journal of Machine Learning & Computing, vol. 4, 2014.
- H. Cunningham, D. Maynard, K. Bontcheva, V. Tablan, N. Aswani, I. Roberts, et al., "Developing language processing components with gate version 6 (a user guide)," University of Sheffield, UK, Web: http://gate. ac. uk/sale/tao/index.html, 2013.
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