Advertisement

Artificial Immune System for Associative Classification

  • Tien Dung Do
  • Siu Cheung Hui
  • Alvis C. M. Fong
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3611)

Abstract

Artificial Immune Systems (AIS), which are inspired from nature immune system, have recently been investigated for many information processing applications, such as feature extraction, pattern recognition, machine learning and data mining. In this paper, we investigate AIS, and in particular the clonal selection algorithm for Associative Classification (AC). To implement associative classification effectively, we need to tackle the problems on the very large search space of candidate rules during the rule mining process. This paper proposes a new approach known as AIS-AC for mining association rules effectively for classification. In AIS-AC, we treat the rule mining process as an optimization problem of finding an optimal set of association rules according to some predefined constraints. The proposed AIS-AC approach is efficient in dealing with the complexity problem on the large search space of rules. It avoids searching greedily for all possible association rules, and is able to find an effective set of associative rules for classification.

Keywords

Association Rule Rule Mining Frequent Itemset Association Rule Mining Artificial Immune System 
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.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    de Castro, L.N., Von Zuben, F.J.: Artificial Immune Systems: Part I – Basic Theory and Applications. Technical Report – RT DCA (1999)Google Scholar
  2. 2.
    de Castro, L.N., Von Zuben, F.J.: The Clonal Selection Algorithm with Engineering Applications. In: Proc. of GECCO 2000. Workshop on Artificial Immune Systems and Their Applications, pp. 36–37 (2000)Google Scholar
  3. 3.
    Liu, B., Hsu, W., Ma, Y.: Integrating classification and association rule mining. In: Proc. of the Fourth International Conference on Knowledge Discovery and Data Mining, New York, pp. 80–86 (1998)Google Scholar
  4. 4.
    Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on multiple class-association rules. In: Proc. of ICDM (2001)Google Scholar
  5. 5.
    Agrawal, R., Srikant, R.: Fast algorithms for mining association rules. In: Proc. of the 20th Int’l Conf. on Very Large Databases (VLDB 1994), Santiago, Chile, pp. 487–499 (1994)Google Scholar
  6. 6.
    Han, J., Pei, J., Yin, Y.: Mining frequent patterns without candidate generation. In: Proc. of the 2000 ACM-SIGMOD International Conference on Management of Data, Dallas, Texas, USA (2000)Google Scholar
  7. 7.
    de Castro, L.N., Von Zuben, F.J.: Learning and Optimization Using the Clonal Selection Principle. IEEE Transactions on Evolutionary Computation, Special Issue on Artificial Immune Systems 6(3), 239–251 (2002)Google Scholar
  8. 8.
    de Castro, L.N., Timmis, J.I.: Artificial Immune Systems as a Novel Soft Computing Paradigm. Soft Computing 7(8), 526–544 (2003)Google Scholar
  9. 9.
    Goldberg, D.E.: Genetic Algorithms in Search, Optimization and Machine Learning. Addison-Wesley, Reading (1989)zbMATHGoogle Scholar
  10. 10.
    Watkins, A.B., Boggess, L.C.: A Resource Limited Artificial Immune Classifier. In: Proc. of Congress on Evolutionary Computation, Part of the 2002 IEEE World Congress on Computational Intelligence, Honolulu, USA, pp. 926–931 (2002)Google Scholar
  11. 11.
    Potter, M., De Jong, K.: The coevolution of antibodies for concept learning. In: Eiben, A.E., Bäck, T., Schoenauer, M., Schwefel, H.-P. (eds.) PPSN 1998. LNCS, vol. 1498, pp. 530–540. Springer, Heidelberg (1998)CrossRefGoogle Scholar
  12. 12.
    Hegland, M.: Algorithms for association rules. Advanced lectures on machine learning. Springer, New York (2003)Google Scholar
  13. 13.
    UCI Machine Learning Repository. Available online at http://www.ics.uci.edu/~mlearn/MLRepository.html
  14. 14.
    Kohavi, R., John, G., Long, R., Manley, D., Pfleger, K.: MLC++: A machine learning library in C++. Tools with Artificial Intelligence, 740–743 (1994)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Tien Dung Do
    • 1
  • Siu Cheung Hui
    • 1
  • Alvis C. M. Fong
    • 1
  1. 1.School of Computer EngineeringNanyang Technological UniversitySingapore

Personalised recommendations