FIRST: FRAMEWORK TO INTEGRATE RELATIONSHIP SEARCH TOOLS

Li Ding, Dana Steil, Brandon Dixon, Nicholas A. Kraft, David B. Brown, and Allen Parrish

References

  1. [1] H. Chen, W. Chung, J.J. Xu, G. Wang, Y. Qin, and M. Chau, Crime data mining: a general framework and some examples, IEEE Computer, 37(4), 2004, 50–56.
  2. [2] S.V. Nath, Crime pattern detection using data mining, Proc. IEEE/WIC/ACM Int. Conf. on Web Intelligence and Intelligent Agent Technology, 2006, 41–44.
  3. [3] J.J. Xu and H. Chen, Fighting organized crimes: using shortestpath algorithms to identify associations in criminal networks, Decision Support Systems, 38, 2004, 473–487.
  4. [4] L. Ding, D. Steil, M. Hudnall, B. Dixon, R. Smith, D. Brown, and A. Parrish, PerpSearch: an integrated crime detection system, Proc. IEEE Intelligence and Security Informatics 2009, Dallas, TX, 2009.
  5. [5] Law Enforcement Tactical System, The University of Alabama Center For Advanced Public Safety Website,
  6. [6] L. Ding and B. Dixon, Using an edge-dual graph and kconnectivity to identify strong connections in social networks, Proc. ACM SE 2008, Auburn, AL, 2008, 475–480.
  7. [7] D. Canter, Geographical profiling of criminals, Medico-legal, 72(pt 2), 2004, 53–66.
  8. [8] D.V. Canter and A. Gregory, Identifying the residential location of rapists, Journal of the Forensic Science Society, 34, 1994, 169–175.
  9. [9] D.K. Rossmo, Place, space and police investigations: hunting serial violent criminals, in D. Weisburd & J.E. Eck (eds.), Crime and place, 1995, 217–235.
  10. [10] L.H. David Canter, A comparison of the efficacy of different decay functions in geographical profiling for a sample of US serial killers, Journal of Investigative Psychology and Offender Profiling, 3(2), 2006, 91–103.
  11. [11] D. Canter and L. Hammond, Prioritizing burglars: comparing the effectiveness of geographical profiling methods, Police Practice and Research, 8(4), 2007, 371–384.
  12. [12] R.V. Hauck, H. Atabakhsb, P. Ongvasith, H. Gupta, and H. Chen, Using Coplink to analyze criminal-justice data, IEEE Computer, 35(3), 2002, 30–37.
  13. [13] D.J. Watts and S. Strogatz, Collective dynamics of ‘smallworld’ networks, Nature, 393(6684), 1998, 440–442.
  14. [14] G. Kossinets and D.J. Watts, Empirical analysis of an evolving social network, Science, 311(5757), 2006, 88–90.
  15. [15] L. Backstorm, D. huttenlocher, J. Kleinberg, and X. Lan, Group formation in large social networks: membership, growth and evolution, Proc. 12th ACM SIGKDD, Philadelphia, PA, 2006, 44–54.
  16. [16] S. Kaza, D. Hu, and H. Chen, Dynamic social network analysis of a dark network: identifying significant facilitators, Proc. IEEE Intelligence and Security Informatics, New Brunswick, NJ, 2007, 40–46. 123
  17. [17] S.R. Safavian and D. Landgrebe, A survey of decision tree classifier methodology, IEEE Transactions on Systems, Man, and Cybernetics, 21, 1991, 660–674.
  18. [18] H. Chen, H. Atabakhsh, C. Tseng, B. Marshall, S. Kaza, S. Eggers, H. Gowda, A. Shah, T. Petersen, and C. Violette, Visualization in law enforcement, Proc. Human Factors in Computing Systems, Portland, OR, 2001, 1268–1271.
  19. [19] The ESRI website,
  20. [20] N. Levine, CrimeStat: a spatial statistics program for the analysis of crime incident locations (v 3.1), Ned Levine & Associates, Houston, TX, and the National Institute of Justice, 2007.
  21. [21] B. Ostrom, M. Kleiman, F. Chessman, R. Hansen, and N. Kauder, Offender risk assessment in Virginia: a three-stage evaluation, National Center for State Courts, 2002.
  22. [22] R. Barnoski, and S. Aos, Washington’s Offender Accountability Act: an analysis of the Department of Corrections’ Risk Assessment, (Olympia, Washington: State Institute for Public Policy), 2003.
  23. [23] Recidivism of Prisoners Released in 1994, P. Langan and D. Levin, June 2, 2002 NCJ 193427, Bureau of Justice Statistics Website,
  24. [24] I.H. Witten and E. Frank, Data mining: practical machine learning tools and techniques, 2nd ed. (San Francisco, CA: Morgan Kaufmann, 2005).
  25. [25] M. Laukkanen and P. Santtila, Predicting the residential location of a serial commercial robber, Forensic Science International, 157(1), 2005, 71–82.
  26. [26] B. Snook, Individual differences in distance travelled by serial burglars, Journal of Investigative Psychology and Offender Profiling, 1(1), 2004, 53–66.
  27. [27] D. Whitley, A genetic algorithm tutorial, Statistics and Computing, 4(2), 1994, 65–85.

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