Abstract
Intelligence operation against the terrorist network has been studied extensively with the aim to mine the clues and traces of terrorists. The contributions of this paper include: (1) introducing a new approach to classify terrorists based on Gene Expression Programming (GEP); (2) analyzing the characteristics of the terrorist organization, and proposing an algorithm called Create Virtual Community (CVC) based on tree-structure to create a virtual community; (3) proposing a formal definition of Virtual Community (VC) and the VCCM Mining algorithm to mine the core members of a virtual community. Experimental results demonstrate the effectiveness of VCCM Mining.
This work was supported by National Science Foundation of China (60473071), Specialized Research Fund for Doctoral Program by the Ministry of Education (20020610007).
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Wentz, L.K., Lee, W.: Wagenhals:Effects Based Operations for Transnational Terrorist Organizations: Assessing Alternative Courses of Action to Mitigate Terrorist Threats. In: Proceedings of Command and Control Research and Technology Symposium, San Diego (2004)
Zhou, C., Xiao, W., Nelson, P.C., Tirpak, T.M.: Evolving Accurate and Compact Classification Rules with Gene Expression Programming. IEEE Transactions on Evolutionary Computation 7(6), 519–531 (2003)
Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, Angra do Heroismo, Portugal (2002)
Matt Crenson: Math wizards offer help in fighting terrorism (2004), http://www.azstarnet.com/dailystar/relatedarticles/42692.php
Qiao, S., Tang, C., Yu, Z., Wei, J., Li, H., Wu, L.: Mining Virtual Community Structure Based on SVM. Computer Science 32(7), 208–212 (2005)
Peng, J., Tang, C., Zhang, J., Yuan, C.: Evolutionary Algorithm Based on Overlapped Gene Expression. In: Wang, L., Chen, K., S. Ong, Y. (eds.) ICNC 2005. LNCS, vol. 3612, pp. 194–204. Springer, Heidelberg (2005)
Ferreira, C.: Gene Expression Programming in Problem Solving. Soft Computing and Industry: Recent Applications, pp. 635–654. Springer, Heidelberg (2002)
Platt, J.: Sequential minimal optimization: A fast algorithm for training support vector machines. Advances in Kernel Methods-Support Vector learning, pp. 185–208. MIT Press, Cambridge (1999)
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Qiao, S., Tang, C., Peng, J., Fan, H., Xiang, Y. (2006). VCCM Mining: Mining Virtual Community Core Members Based on Gene Expression Programming. In: Chen, H., Wang, FY., Yang, C.C., Zeng, D., Chau, M., Chang, K. (eds) Intelligence and Security Informatics. WISI 2006. Lecture Notes in Computer Science, vol 3917. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11734628_16
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DOI: https://doi.org/10.1007/11734628_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33361-6
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