[1] L. Genzman, Responding to organized crime: Laws and law
enforcement. Organized crime, In H.Abadinsky (Ed.) Belmont, CA:
Wadsworth, pp. 342.
[2] R. Baker, The biggest loophole in the free-market system. Washington
Quarterly, 22, 1999, pp. 29-46.
[3] R. C. Watkins et al, Exploring Data Mining technologies as Tool to
Investigate Money Laundering. Journal of Policing Practice and
Research: An International Journal. Vol. 4, No. 2, January 2003, pp.
163-178.
[4] J. Han and M. Kamber, Data Mining: Concept and Techniques. Morgan
Kaufmann publishers, 2nd Eds., Nov. 2005.
[5] J. Tang, J. Yin, Developing an intelligent data discriminating system of
anti-money laundering based on SVM, Proceedings of the Four
International Conference on Machine Learning and Cybernetics,
Guangzhou, Aug. 2005: pp.3453-3457.
[6] Z. Zang, J.J. Salermo and P. S. Yu, Applying Data mining in
Investigating Money Laundering Crimes, SIGKDD-03, August 2003,
Washington DC, USA. pp: 747-752.
[7] N-A. Le-Khac, S. Markos, M. O'Neill, A. Brabazon and M-T. Kechadi,
An Efficient Search Tool for an Anti-Money Laundering Application of
an Multi-National Bank's Dataset, The 2009 International Conference
on Information and Knowledge Engineering, July 13-16, 2009 (IKE
2009), LA, USA.
[8] N-A. Le-Khac, S. Markos and M-T. Kechadi, Towards a new Data
Mining-based approach for Anti Money laundering in an international
investment bank. a NY, USA (to appear).
[9] R. Jain, R. Kasturi and B.G. Schunck, Machine Vision, Prentice Hall,
1995.
[10] B. Scholkopf, A short tutorial on kernels. Microsoft Research, Tech
Rep: MSR-TR-200-6t, 2000.
[11] J. Kingdon, AI Fights Money Laundering, IEEE Transactions on
Intelligent Systems, 2004, pp. 87-89.
[12] B. Scholkopf and J. Plattz, Estimating the support of a high dimensional
distribution, Neural Computing, Vol. 13, No. 7, 2001: pp1443-1472.
[13] D.R Wilson and T. R. Martinez, Improved Heterogeneous distance
functions. Journal of Artificial Intelligence Research, Vol. 6, No. 1,
1997: pp 1-34.
[14] J. Tang, A Framework on Developing an Intelligent Discriminating
System of Anti Money Laundering, International Conference on
Financial and Banking, Czech Rep., 2005
[15] G.S. Vidyashankar, R. Natarajan and S. Sanyal, Mining your way to
combat money laundering. DM Review Special Report, Oct 2007.
[16] G. Gan, C. Ma and J. Wu, Data Clustering: Theory, Algorithms and
Applications. Siam publishers 2007, pp 161-182
[1] L. Genzman, Responding to organized crime: Laws and law
enforcement. Organized crime, In H.Abadinsky (Ed.) Belmont, CA:
Wadsworth, pp. 342.
[2] R. Baker, The biggest loophole in the free-market system. Washington
Quarterly, 22, 1999, pp. 29-46.
[3] R. C. Watkins et al, Exploring Data Mining technologies as Tool to
Investigate Money Laundering. Journal of Policing Practice and
Research: An International Journal. Vol. 4, No. 2, January 2003, pp.
163-178.
[4] J. Han and M. Kamber, Data Mining: Concept and Techniques. Morgan
Kaufmann publishers, 2nd Eds., Nov. 2005.
[5] J. Tang, J. Yin, Developing an intelligent data discriminating system of
anti-money laundering based on SVM, Proceedings of the Four
International Conference on Machine Learning and Cybernetics,
Guangzhou, Aug. 2005: pp.3453-3457.
[6] Z. Zang, J.J. Salermo and P. S. Yu, Applying Data mining in
Investigating Money Laundering Crimes, SIGKDD-03, August 2003,
Washington DC, USA. pp: 747-752.
[7] N-A. Le-Khac, S. Markos, M. O'Neill, A. Brabazon and M-T. Kechadi,
An Efficient Search Tool for an Anti-Money Laundering Application of
an Multi-National Bank's Dataset, The 2009 International Conference
on Information and Knowledge Engineering, July 13-16, 2009 (IKE
2009), LA, USA.
[8] N-A. Le-Khac, S. Markos and M-T. Kechadi, Towards a new Data
Mining-based approach for Anti Money laundering in an international
investment bank. a NY, USA (to appear).
[9] R. Jain, R. Kasturi and B.G. Schunck, Machine Vision, Prentice Hall,
1995.
[10] B. Scholkopf, A short tutorial on kernels. Microsoft Research, Tech
Rep: MSR-TR-200-6t, 2000.
[11] J. Kingdon, AI Fights Money Laundering, IEEE Transactions on
Intelligent Systems, 2004, pp. 87-89.
[12] B. Scholkopf and J. Plattz, Estimating the support of a high dimensional
distribution, Neural Computing, Vol. 13, No. 7, 2001: pp1443-1472.
[13] D.R Wilson and T. R. Martinez, Improved Heterogeneous distance
functions. Journal of Artificial Intelligence Research, Vol. 6, No. 1,
1997: pp 1-34.
[14] J. Tang, A Framework on Developing an Intelligent Discriminating
System of Anti Money Laundering, International Conference on
Financial and Banking, Czech Rep., 2005
[15] G.S. Vidyashankar, R. Natarajan and S. Sanyal, Mining your way to
combat money laundering. DM Review Special Report, Oct 2007.
[16] G. Gan, C. Ma and J. Wu, Data Clustering: Theory, Algorithms and
Applications. Siam publishers 2007, pp 161-182