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Original Articles

Homicide and Its Social Context: Analysis Using the Self-Organizing Map

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REFERENCES

  • Adderley, R. 2004. The use of data mining techniques in operational crime fighting. In Intelligence and security informatics, Part 2, LNCS 3073:418–425. Berlin, Heidelberg: Springer.
  • Adderley, R., and P. Musgrave. 2005. Modus operandi modelling of group offending: A data-mining case study. International Journal of Police Science and Management 5(4):265–276.
  • Axelsson, S. 2005. Understanding intrusion detection through visualization (PhD thesis, Chalmers University of Technology, Göteborg, Sweden).
  • Brockett, P. L., X. Xia, and R. A. Derrig. 1998. Using Kohonen’s self-organizing feature map to uncover automobile bodily injury claims fraud. The Journal of Risk and Insurance 65(2):245–274.
  • Burges, C. J. C. 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery 2(2):121–167.
  • Cortes, C., and V. Vapnik. 1995. Support-vector networks. Machine Learning 20(3):273–297.
  • Fei, B., J. Eloff, M. Olivier, and H. Venter. 2006. The use of self-organizing maps for anomalous behavior detection in a digital investigation. Forensic Science International 162(1–3):33–37.
  • Fei, B., J. Eloff, H. Venter, and M. Olivier. 2005. Exploring data generated by computer forensic tools with self-organising maps. Proceedings of the IFIP Working Group 11.9 on Digital Forensics (2005)1–15.
  • Galar, M., A. Fernández, E. Barrenechea, H. Bustince, and F. Herrera. 2011. An overview of ensemble methods for binary in multi-class problems: Experimental study on one-vs-one and one-vs-all schemes. Pattern Recognition 44(8):1761–1776.
  • Grosser, H., P. Britos, and R. García-Martínez. 2005. Detecting fraud in mobile telephony using neural networks. In Innovations in Applied Artificial Intelligence, Lecture Notes in Artificial Intelligence, 3533: 613–615,ed. M. Ali and F. Esposito. Berlin, Germany: Springer-Verlag.
  • Hollmén, J. 2000. User profiling and classification for fraud detection in mobile communications networks ( PhD thesis, Helsinki University of Technology, Finland).
  • Hollmén, J., V. Tresp, and O. Simula. 1999. A self-organizing map for clustering probabilistic models. Artificial Neural Networks 470:946–951.
  • Hsu, C.-W., C.-C. Chang, and C.-C. Lin. 2013. A practical guide to support vector classification (Technical report). Taiwan: National Taiwan University. Available at: http://www.csie.ntu.edu.tw/˜cjlin/articles/guide/guide.pdf.
  • Joutsijoki, H., and M. Juhola. 2011. Comparing the one-vs-one and one-vs-all methods in benthic macroinvertebrate image classification. In Machine learning and data mining in pattern recognition, Lecture Notes in Artificial Intelligence 6871: 399–413,ed. P. Perner. Berlin, Germany: Springer-Verlag.
  • Joutsijoki, H., and M. Juhola. 2013. Kernel selection in multi-class support vector machines and its consequence to the number of ties in majority voting method. Artificial Intelligence Review 40(3):213–230.
  • Juhola, M., and M. Siermala. 2012a. A scatter method for data and variable importance evaluation. Integrated Computer-Aided Engineering 19(2):137–149.
  • Juhola, M., and M. Siermala. 2012b. ScatterCounter software. Available at http://www.uta.fi/sis/cis/research_groups/darg/publications.html.
  • Kangas, L. J. 2001. Artificial neural network system for classification of offenders in murder and rape cases. Finland: National Institute of Justice.
  • Kangas, L. J., K. M. Terrones, R. D. Keppel, and R. D. La Moria. 1999. Computer-aided tracking and characterization of homicides and sexual assaults (CATCH). In Proceedings of SPIE 3722, Applications and Science of Computational Intelligence II (March 22, 1999), Orlando, Florida, USA.
  • Kohonen, T. 1979. Self-organizing maps. New York, NY: Springer-Verlag.
  • Lampinen, T., H. Koivisto, and T. Honkanen. 2005. Profiling network applications with fuzzy c-means and self-organizing maps. Classification and Clustering for Knowledge Discovery 4:15–27.
  • Leufven, C. 2006. Detecting ssh identity theft in hpc cluster environments using self-organizing maps (Master’s thesis, Linköping University, Sweden).
  • Memon, Q. A., and S. Mehboob. 2006. Crime investigation and analysis using neural nets. In Proceedings of international joint conference on neural networks, 346–350. Washington, DC: IEEE.
  • Rock, R. 1994. History of criminology. Aldershot, UK: Dartmouth.
  • United Nations Office on Drugs and Crime (UNODC). 2011. Global study on homicide – trends, contexts, data. Vienna: United Nations Office of Drugs and Crime. Available at http://www.unodc.org/.../Homicide/Globa_study_on_homicide_2011_web.pdf
  • Suykens, J. A. K., and J. Vandewalle. 1999. Least squares support vector machine classifiers. Neural Processing Letters 9:293–300.
  • Vapnik, V. N. 2000. The nature of statistical learning theory (2nd ed.). New York, NY: Springer-Verlag.
  • Zaslavsky, V., and A. Strizhak. 2006. Credit card fraud detection using self-organizing maps. Information and Security: An International Journal 18:48–63.

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