Learning Using a Self-building Associative Frequent Network

  • Jin-Guk Jung
  • Mohammed Nazim Uddin
  • Geun-Sik Jo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4432)

Abstract

In this paper, we propose a novel framework, called a frequent network, to discover frequent itemsets and potentially frequent patterns by logical inference. We also introduce some new terms and concepts to define the frequent network, and we show the procedure of constructing the frequent network. We then describe a new method LAFN (Learning based on Associative Frequent Network) for mining frequent itemsets and potentially patterns, which are considered as a useful pattern logically over the frequent network. Finally, we present a useful application, classification with these discovered patterns from the proposed framework, and report the results of the experiment to evaluate our classifier on some data sets.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Agrawal, R., Imielinski, T., Swami, A.: Mining association rules between sets of items in large databases. In: Proceedings of the 1993 ACM SIGMOD international conference on Management of data, pp. 207–216 (1993)Google Scholar
  2. 2.
    Amari, S.I.: Learning patterns and pattern sequences by self-organizing nets. IEEE Transactions on Computer 21(11), 1197–1206 (1972)MATHCrossRefMathSciNetGoogle Scholar
  3. 3.
    Fayyad, U.M., Irani, K.B.: Multi-interval discretization of continuous-valued attributes for classification learning. In: IJCAI-93, pp. 1022–1027 (1993)Google Scholar
  4. 4.
    Hebb, D.O.: The Organization of Behaviour. John Wiley, New York (1949)Google Scholar
  5. 5.
    Hopfield, J.: Neural networks and physical systems with emergent collective computational abilities. Proc. Natl. Acad. Sci. USA 79(8), 2554–2558 (1982)CrossRefMathSciNetGoogle Scholar
  6. 6.
    Kohavi, R., John, G., Long, R., Manley, D., Pfleger, K.: MLC++: a machine learning library in C++. In: Tools with artificial intelligence, pp. 740–743 (1994)Google Scholar
  7. 7.
    Li, W., Han, J., Pei, J.: CMAR: Accurate and efficient classification based on Multiple Class-Association Rules. IEEE Computer Society Press, Los Alamitos (2001)Google Scholar
  8. 8.
    Liu, B., Hsu, W., Ma, Y.: Integrating calssification and association rule mining. In: KDD’98, New York, NY (Aug. 1998)Google Scholar
  9. 9.
    Newman, D.J., Hettich, S., Blake, C.L., Merz, C.J.: UCI Repository of machine learning databases. University of California, Department of Information and Computer Science, Irvine, CA (1998), http://www.ics.uci.edu/~mlearn/MLRepository.html
  10. 10.
    Quinlan, J.R.: C4.5: Programs for machine Learning. Morgan Kaufmann, San Francisco (1993)Google Scholar

Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • Jin-Guk Jung
    • 1
  • Mohammed Nazim Uddin
    • 1
  • Geun-Sik Jo
    • 2
  1. 1.Intelligent e-Commerce Systems Laboratory, 253 Yonghyun-Dong, Nam-Gu, Incheon, 402-751Korea
  2. 2.School of Computer Engineering, Inha University, 253 Yonghyun-Dong, Nam-Gu, Incheon, 402-751Korea

Personalised recommendations