Terrorist Organization Behavior Prediction Algorithm Based on Context Subspace

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7121)


Researchers have developed methods to predict a terrorist organization’s probable actions (such as bombings or kidnappings). The terrorist organization’s actions can be affected by the context of the organization. Thus, the organization’s context variables can be used to improve the accuracy of forecasting the terrorist behavior. Those algorithms based on context similarity suffer a serious drawback that it can result in the algorithm’s fluctuation and reduce the prediction accuracy if not all the attributes are detected. A prediction algorithm PBCS (Prediction Based on Context Subspace) based on context subspace is proposed in this paper. The proposed algorithm first extracts the context subspace according to the association between the context attributes and the behavior attributes. Then, it predicts the terrorist behavior based on the extracted context subspace. The proposed algorithm uses the improved spectral clustering method to obtain the context subspace. It concerns the distribution of the data samples, the label information and the local similarity of the data in the process of extracting the subspace. Experimental results on the artificial dataset and the MAROB dataset show that the prediction method proposed in this paper can not only improve the prediction accuracy but also reduce the prediction fluctuation.


terrorism behavior prediction context knowledge context subspace spectral analysis 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wang, F.Y.: Is Culture Computable. IEEE Intelligent Systems 24(2), 2–3 (2009)CrossRefGoogle Scholar
  2. 2.
    Wang, F.Y., Carley, K.M., Zeng, D., Mao, W.: Social Computing: from Social Informatics to Social Intelligence. IEEE Intelligent Systems 22(2), 79–83 (2007)CrossRefGoogle Scholar
  3. 3.
    Zeng, D., Wang, F.Y., Carley, K.M.: Social Computing. IEEE Intelligent Systems 22(5), 20–22 (2007)CrossRefGoogle Scholar
  4. 4.
    Subrahmanian, V.S.: Culture Modeling in Real Time. Science 317(5844), 1509–1510 (2007)CrossRefGoogle Scholar
  5. 5.
    Subrahmanian, V.S., Massimiliano, A., Vanina, M.M., et al.: CARA: A Cultural Reasoning Architecture. IEEE Intelligent Systems 22(2), 12–15 (2007)CrossRefGoogle Scholar
  6. 6.
    Samir, K., Martinez, V., Nau, D., et al.: Finding Most Probable Worlds of Probabilistic Logic Programs. In: 1st International Conference on Scalable Uncertainty Management, pp. 45–57. IEEE Press, Washington DC (2007)Google Scholar
  7. 7.
    Xiaochen, L., Wenji, M., Daniel, Z., et al.: Performance Evaluation of Machine Learning Methods in Cultural Modeling. Journal of Computer Science and Technology 24, 1010–1017 (2009)CrossRefGoogle Scholar
  8. 8.
    Vanina, M., Simari, G.I., Sliva, A., et al.: CONVEX: Similarity-based Algorithms for Forecasting Group Behavior. IEEE Intelligent Systems 23(4), 51–57 (2008)CrossRefGoogle Scholar
  9. 9.
    Daoqiang, Z., Songcan, C.: High-dimensional Data Reduction Methods. Communications of the CCF 5(8), 15–22 (2009) (in Chinese)Google Scholar
  10. 10.
    Jolliffe, I.T.: Principal Component Analysis. Springer, New York (2002)zbMATHGoogle Scholar
  11. 11.
    Sam, R.T., Lawrence, S.: Nonlinear Dimensionality Reduction by Local Linear Embedding. Science 290(5500), 2323–2326 (2000)CrossRefGoogle Scholar
  12. 12.
    Fan, C.: Spectral Graph Theory. American Mathematical Society, Providence (1997)zbMATHGoogle Scholar
  13. 13.
    Xiaoyan, C., Guanzhong, D., Libin, Y.: Survey on Spectral Clustering Algorithm. Computer Science 35(7), 14–18 (2008) (in Chinese)Google Scholar
  14. 14.
    Minorities at Risk Project,
  15. 15.
    Bach, F.R., Jordan, M.I.: Learning Spectral Clustering with Application to Speech Separation. Journal of Machine Learning Research 7, 1963–2001 (2006)zbMATHGoogle Scholar
  16. 16.
    Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, New York (2006)zbMATHGoogle Scholar
  17. 17.
    Guyon, I., Elisseeff, A.: An Introduction to Variable and Feature Selection. Journal of Machine Learning Research 3, 1157–1182 (2003)zbMATHGoogle Scholar
  18. 18.
    Qiu, X., Wu, L.: Stepwise Nearest Neighbor Discriminate Analysis. In: IJCAI, pp. 829–834. Morgan Kaufmann Press, San Francisco (2005)Google Scholar
  19. 19.
    Haw-Ren, F., Sophia, S., Yousef, S.: Multilevel Manifold Learning with Application to Spectral Clustering. In: CIKM 2010, pp. 419–428. ACM Press, New York (2010)Google Scholar
  20. 20.
    Donghui, Y., Ling, H., Jordan, M.: Fast Approximate Spectral Clustering. In: 15th ACM Conference on Knowledge Discovery and Data Mining, pp. 907–915. ACM Press, New York (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.School of Computer Science and Telecommunication EngineeringJiangsu UniversityZhenjiangChina

Personalised recommendations