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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 308675, 10 pages
Crime Busting Model Based on Dynamic Ranking Algorithms
1College of Telecommunications and Information Engineering, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
2College of Overseas Education, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
3School of Engineering and Computing Sciences, New York Institute of Technology, Old Westbury, NY 11568-8000, USA
Received 28 May 2013; Accepted 11 June 2013
Academic Editor: Xinsong Yang
Copyright © 2013 Yang Cao et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
- V. E. Krebs, “Mapping networks of terrorist cells,” Connections, vol. 24, pp. 43–52, 2002.
- C. Morselli, “Assessing vulnerable and strategic positions in a criminal network,” Journal of Contemporary Criminal Justice, vol. 26, no. 4, pp. 382–392, 2010.
- J. Xu and H. Chen, “Untangling criminal networks: a case study,” in Intelligence and Security Informatics, vol. 2665, pp. 232–248, 2003.
- C. Arney and K. Coronges, “Judges' commentary: modeling for crime busting,” UMAP Journal, vol. 33, no. 3, p. 293, 2012.
- COMAP, “2012 ICM problem,” 2012, http://www.comap.com/undergraduate/contests/mcm/contests/2012/problems/.
- J. F. Sowa, “Semantic networks,” in Encyclopedia of Cognitive Science, 2006.
- R. Feldman and J. Sanger, The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data, Cambridge University Press, 2006.
- R. Bilisoly, Practical Text Mining with Perl, vol. 2, John Wiley & Sons, Hoboken, NJ, USA, 2008.
- M. Sharples, D. Hogg, C. Hutchinson, S. Torrance, and D. Young, Computers and Thought: A Practical Introduction to Artificial Intelligence, MIT Press, 1989.
- U. Gretzel, “Social network analysis: introduction and resources,” 2001, http://lrs.ed.uiuc.edu/tse-portal/analysis/social-network-analysis/.
- Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer, “An efficient boosting algorithm for combining preferences,” Journal of Machine Learning Research, vol. 4, no. 6, pp. 933–969, 2003.
- A. Meaden and D. Hacker, Problematic and Risk Behaviours in Psychosis: A Shared Formulation Approach, Routledge, 2010.
- J. A. Zhang and X. E. Guo, “Trust model based on dynamic recommendation in P2P network,” Computer Engineering, vol. 36, no. 1, pp. 174–180, 2010.
- F. Autrel, N. Cuppens-Boulahia, and F. Cuppens, “Reaction policy model based on dynamic organizations and threat context,” in Data and Applications Security XXIII, vol. 5645 of Lecture Notes in Computer Science, pp. 49–64, Springer, 2009.
- Y. J. Tan, J. Wu, and H. Z. Deng, “Evaluation method for node importance based on node contraction in complex networks,” System Engineering Theory and Practice, vol. 26, no. 11, pp. 79–83, 2006.
- M. Perkowitz and O. Etzioni, “Adaptive web sites: conceptual cluster mining,” in Proceedings of the 16th International Joint Conference on Artificial Intelligence, pp. 264–269, July 1999.
- R. Cooley, B. Mobasher, and J. Srivastava, “Web mining: information and pattern discovery on the World Wide Web,” in Proceedings of the IEEE 9th IEEE International Conference on Tools with Artificial Intelligence, pp. 558–567, November 1997.
- J. M. Kleinberg, “Authoritative sources in a hyperlinked environment,” Journal of the ACM, vol. 46, no. 5, pp. 604–632, 1999.
- G. W. Flake, S. Lawrence, and C. L. Giles, “Efficient identification of web communities,” in Proceedings of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 150–160, August 2000.