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Suspects Investigation

  • Mohammad A. Tayebi
  • Uwe Glässer
Chapter
Part of the Lecture Notes in Social Networks book series (LNSN)

Abstract

In their efforts to identify potential suspects, crime investigators routinely draw on partial knowledge as the result of incomplete information and uncertain clues. Physical evidence gathered at a crime scene as well as accounts from victims and witnesses may be incomplete and inconclusive. In cases with multiple offenders jointly committing a crime, where individual suspects have been identified, the aim of co-offending network analysis is to complement criminal profiling methods (Baumgartner et al, Knowl-Based Syst 21(7):563–572, 2008; Ferrari et al, IEEE Control Syst Mag 28(4):65–77, 2008) so as to identify additional suspects faster and more effectively, thus decreasing the cost and time of crime investigations.

Keywords

Random Walk Association Rule Association Rule Mining Link Prediction Criminal Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 1.
    R. Agrawal, T. Imieliński, A. Swami, Mining association rules between sets of items in large databases. ACM SIGMOD Record 22 (2), 207–216 (1993)CrossRefGoogle Scholar
  2. 2.
    R. Agrawal, R. Srikant, Fast algorithms for mining association rules in large databases, in Proceedings of the 20th International Conference on Very Large Data Bases, VLDB’94, pp. 487–499, 1994Google Scholar
  3. 3.
    K. Baumgartner, S. Ferrari, G. Palermo, Constructing Bayesian networks for criminal profiling from limited data. Knowl.-Based Syst. 21 (7), 563–572 (2008)CrossRefGoogle Scholar
  4. 4.
    A.A. Block, East Side-West Side: Organizing Crime in New York City, 1930–1950 (Transaction Publishers, New Brunswick, 1994)Google Scholar
  5. 5.
    R. Boba, Crime Analysis and Crime Mapping (SAGE Publications, Thousand Oaks, 2013)Google Scholar
  6. 6.
    S. Ferrari, K. Baumgartner, G. Palermo, R. Bruzzone, M. Strano, Network models of criminal behavior. IEEE Control. Syst. Mag. 28 (4), 65–77 (2008)MathSciNetCrossRefGoogle Scholar
  7. 7.
    X. Fu, J. Budzik, K.J. Hammond, Mining navigation history for recommendation, in Proceedings of the 5th International Conference on Intelligent User Interfaces (IUI’00), pp. 106–112, 2000Google Scholar
  8. 8.
    M. Gori, A. Pucci, Itemrank: a random-walk based scoring algorithm for recommender engines, in Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI’07), pp. 2766–2771, 2007Google Scholar
  9. 9.
    P. Hájek, I. Havel, M. Chytil, The Guha Method of Automatic Hypotheses Determination, vol. 1 (Springer, Berlin, 1966), pp. 293–308zbMATHGoogle Scholar
  10. 10.
    J. Han, M. Kamber, Data Mining: Concepts and Techniques. (Morgan Kaufmann, New York, 2006)Google Scholar
  11. 11.
    M. Jamali, M. Ester, TrustWalker: a random walk model for combining trust-based and item-based recommendation, in Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’09), pp. 135–142, 2009Google Scholar
  12. 12.
    M. Jamali, M. Ester, Using a trust network to improve top-N recommendation, in Proceedings of the 3rd ACM Conference on Recommender Systems (RecSys’09), pp. 181–188, 2009Google Scholar
  13. 13.
    P. Lazarsfeld, R. Merton, Friendship as a social process: a substantive and methodological analysis. in Freedom and Control in Modern Society. ed. by T. Abel, M. Berger, C. Page (Van Nostrand, New York, 1954)Google Scholar
  14. 14.
    W. Lin, S.A. Alvarez, C. Ruiz, Efficient adaptive-support association rule mining for recommender systems. Data Min. Knowl. Disc. 6 (1), 83–105 (2002)MathSciNetCrossRefGoogle Scholar
  15. 15.
    D. McAndrew, The structural analysis of criminal networks. in The Social Psychology of Crime: Groups, Teams, and Networks, ed. by D. Canter, L. Alison (Dartmouth Publishing, Hanover, 1999)Google Scholar
  16. 16.
    M. McPherson, L. Smith-Lovin, J.M. Cook, Birds of a feather: homophily in social networks. Annu. Rev. Sociol. 27 (1), 415–444 (2001)CrossRefGoogle Scholar
  17. 17.
    M.A. Tayebi, M. Jamali, M. Ester, U. Glässer, R. Frank, CrimeWalker: a recommendation model for suspect investigation, in Proceedings of the 5th ACM Conference on Recommender Systems (RecSys’11), pp. 173–180, 2011Google Scholar
  18. 18.
    Z. Xia, Fighting criminals: Adaptive inferring and choosing the next investigative objects in the criminal network. Knowl.-Based Syst. 21 (5), 434–442 (2008)CrossRefGoogle Scholar
  19. 19.
    J.J. Xu, H. Chen, Fighting organized crimes: using shortest-path algorithms to identify associations in criminal networks. Int. J. Dec. Support Syst. 38 (3), 473–487 (2004)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Mohammad A. Tayebi
    • 1
  • Uwe Glässer
    • 1
  1. 1.Computing ScienceSimon Fraser UniversityBritish ColumbiaCanada

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