A Scalable Artificial Immune System Model for Dynamic Unsupervised Learning

  • Olfa Nasraoui
  • Fabio Gonzalez
  • Cesar Cardona
  • Carlos Rojas
  • Dipankar Dasgupta
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2723)


Artificial Immune System (AIS) models offer a promising approach to data analysis and pattern recognition. However, in order to achieve a desired learning capability (for example detecting all clusters in a dat set), current models require the storage and manipulation of a large network of B Cells (with a number often exceeding the number of data points in addition to all the pairwise links between these B Cells). Hence, current AIS models are far from being scalable, which makes them of limited use, even for medium size data sets.

We propose a new scalable AIS learning approach that exhibits superior learning abilities, while at the same time, requiring modest memory and computational costs. Like the natural immune system, the strongest advantage of immune based learning compared to current approaches is expected to be its ease of adaptation in dynamic environments. We illustrate the ability of the proposed approach in detecting clusters in noisy data.


Artificial immune systems scalability clustering evolutionary computation dynamic learning 


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  1. 1.
    D. Dasgupta, Artificial Immune Systems and Their Applications, Springer Verlag, 1999.Google Scholar
  2. 2.
    I. Cohen, Tending Adam’s Garden, Academic Press, 2000.Google Scholar
  3. 3.
    J. Hunt and D. Cooke, “An adaptative, distributed learning system, based on immune system,” in IEEE International Conference on Systems, Man and Cybernetics, Los Alamitos, CA, 1995, pp. 2494–2499.Google Scholar
  4. 4.
    L. N. De Castro and F. J. Von Zuben, “An evolutionary immune network for data clustering,” in IEEE Brazilian Symposium on Artificial Neural Networks, Rio de Janeiro, 2000, pp. 84–89.Google Scholar
  5. 5.
    J.D. Farmer and N.H. Packard, “The immune system, adaptation and machne learning,” Physica, vol. 22, pp. 187–204, 1986.MathSciNetGoogle Scholar
  6. 6.
    F.J. Varela H. Bersini, “The immune recruitment mechanism: a selective evolutionary strategy,” in Fourth International Conference on Genetic Algorithms, San Mateo, CA, 1991, pp. 520–526.Google Scholar
  7. 7.
    S. Forrest, A. S. Perelson, L. Allen, and R. Cherukuri, “Self-nonself discrimination in a computer,” in IEEE Symposium on Research in Security and Privacy, Los Alamitos, CA, 1994.Google Scholar
  8. 8.
    D. Dasgupta and S. Forrest, “Novelty detection in time series data using ideas from immunology,” in 5th International Conference on Intelligent Systems, Reno, Nevada, 1996.Google Scholar
  9. 9.
    J. Timmis and M. Neal, “A resource limited artificial immune system for data analysis,” Knowledge Based Systems, vol. 14, no. 3, pp. 121–130, 2001.CrossRefGoogle Scholar
  10. 10.
    T Knight and J Timmis, “Aine: An immunological approach to data mining,” in IEEE International Conference on Data Mining, San Jose, CA, 2001, pp. 297–304.Google Scholar
  11. 11.
    O. Nasraoui, D. Dasgupta, and F. Gonzalez, “An artificial immune system approach to robust data mining,” in Genetic and Evolutionary Computation Conference (GECCO) Late breaking papers, New York, NY, 2002, pp. 356–363.Google Scholar
  12. 12.
    M. Neal, “An artificial immune system for continuous analysis of time-varying data,” in 1st International Conference on Artificial Immune Systems, Canterbury, UK, 2002, pp. 76–85.Google Scholar
  13. 13.
    Wierzchon and U. Kuzelewska, “Stable clusters formation in an artificial immune system,” in 1st International Conference on AIS, Canterbury, UK, 2002, pp. 68–75.Google Scholar
  14. 14.
    E Hart and P Ross, “Exploiting the analogy between immunology and spares distributed memories: A system for clustering non-stationary data,” in 1st International Conference on Artificial Immune Systems, Canterbury, UK, 2002, pp. 49–58.Google Scholar
  15. 15.
    N. K. Jerne, “The immune system,” Scientific American, vol. 229, no. 1, pp. 52–60, 1973.CrossRefGoogle Scholar
  16. 16.
    J. Timmis, M. Neal, and J. Hunt, “An artificial immune system for data analysis,” Biosystems, vol. 55, no. 1, pp. 143–150, 2000.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2003

Authors and Affiliations

  • Olfa Nasraoui
    • 1
  • Fabio Gonzalez
    • 2
  • Cesar Cardona
    • 1
  • Carlos Rojas
    • 1
  • Dipankar Dasgupta
    • 2
  1. 1.Department of Electrical and Computer EngineeringThe University of MemphisMemphis
  2. 2.Division of Computer SciencesThe University of MemphisMemphis

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