A Combinational Clustering Method Based on Artificial Immune System and Support Vector Machine

  • Zhonghua Li
  • Hong-Zhou Tan
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4251)


Clustering is one branch of unsupervised machine learning theory, which has a wide variety of applications in pattern recognition, image processing, economics, document categorization, web mining, etc. Today, we constantly face how to handle a large number of similar data items, which drives many researchers to contribute themselves to this field. Support vector machine provides a new pathway for clustering, however, it behaves bad in handling massive data. As an emergent theory, artificial immune system can effectively recognize antigens and produce the memory antibodies. This mechanism is constantly used to achieve representative or feature data from raw data. A combinational clustering method is proposed in this paper based on artificial immune system and support vector machine. Experimentation in functionality and performance is done in detail. Finally a more challenging application in elevator industry is conducted. The results strongly indicate that this combinational clustering in this paper is of feasibility and of practice.

Indexed Terms: Data clustering, combinational clustering, artificial immune system, support vector machine.


Support Vector Machine Data Item Artificial Immune System Secondary Immune Response Suppression Threshold 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Jajuga, K., Sokolowski, A., Bock, H.H.: Classification, Clustering and Data Analysis. Springer, Berlin, Heidelberg, New York (2002)MATHGoogle Scholar
  2. Asa, B.H., David, H., Hava, T.S., Vapnik, V.: Support Vector Clustering. J. of Machine Learning Research 2, 125–137 (2001)Google Scholar
  3. Jain, A.K., Murty, M.N., Flynn, P.J.: Data Clustering: A Review. ACM Computing Surveys 31(3), 264–363 (1999)CrossRefGoogle Scholar
  4. Timmis, J., Knight, T., de Catro, L.N., Hart, E.: An Overview of artificial immunesystems. In: Computation in Cells and Tissues: Perspectives and Tools Thought. Natural Computation Series, pp. 51–86. Springer, Heidelberg (2004)Google Scholar
  5. Li, Z.H., Zhu, Y.F., Li, C.H., Mao, Z.Y.: Elevator Traffic Flow Analysis Based on Artificial Immune Clustering Algorithm. Chinese Journal of South China University of Technology (Natural Science Edition) 31(12), 26–29 (2003)Google Scholar
  6. de Castro, L.N., von Zuben, F.J.: An Evolutionary Immune System Network for Data Clustering. In: Proceedings of the Sixth Brazilian Symposium on Neural Networks, Rio de Janeiro, pp. 84–89 (2000)Google Scholar
  7. Li, Z.H., Chen, S.B., Zheng, R.R., Wu, J.P., Mao, Z.Y.: A Novel Approach to Clustering Analysis Based on Support Vector Machine. In: Yin, F.-L., Wang, J., Guo, C. (eds.) ISNN 2004. LNCS, vol. 3173, pp. 565–571. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  8. Li, J., Gao, X.B., Jiao, L.C.: A Novel Clustering Algorithm Based on Immune Network with Limited Resource. In: Webb, G.I., Yu, X. (eds.) AI 2004. LNCS (LNAI), vol. 3339, pp. 319–331. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  9. Younsi, R., Wang, W.J.: A New Artificial Immune System Algorithm for Clustering. In: Yang, Z.R., Yin, H., Everson, R.M. (eds.) IDEAL 2004. LNCS, vol. 3177, pp. 58–64. Springer, Heidelberg (2004)CrossRefGoogle Scholar
  10. de Catro, L.N., Timmis, J.: Artificial Immune systems: A New Computational Intelligence Approach. Springer, London (2002)Google Scholar
  11. Li, Z.H., Tan, H.-Z.: An Improved Clustering Method for Large-scale Data Based on artificial Immune systems. In: Dynamics of Continuous, Discrete and Impulsive Systems, Series B: Applications and Algorithms (in press, 2006)Google Scholar
  12. Li, Z.H., Tan, H.Z.: Combining Artificial Immune System with Support Vector Machine for Clustering Analysis. In: Li, Z., Tan, H.-Z. (eds.) Dynamics of Continuous, Discrete and Impulsive ystems, Series B: Applications and Algorithms, 162 (in press, 2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Zhonghua Li
    • 1
  • Hong-Zhou Tan
    • 1
  1. 1.Department of Electronics and Communication EngineeringSun Yat-sen UniversityGuangzhouChina

Personalised recommendations