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Extension of Graph-Based Induction for General Graph Structured Data

  • Takashi Matsuda
  • Tadashi Horiuchi
  • Hiroshi Motoda
  • Takashi Washio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1805)

Abstract

A machine learning technique called Graph-Based Induction (GBI) efficiently extracts typical patterns from directed graph data by stepwise pair expansion (pairwise chunking). In this paper, we expand the capability of the Graph-Based Induction to handle not only tree structured data but also multi-inputs/outputs nodes and loop structure (including a self-loop) which cannot be treated in the conventional way. The method is verified to work as expected using artificially generated data and we evaluated experimentally the computation time of the implemented program. We, further, show the effectiveness of our approach by applying it to two kinds of the real-world data: World Wide Web browsing data and DNA sequence data.

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References

  1. [Agrawal94]
    R. Agrwal and R. Srikant. First Algorithms for Mining Association Rules. Proc. of the 20th VLDB Conference, pp. 487–499, 1994.Google Scholar
  2. [Blake98]
    C. Blake, E. Keogh, and C. J. Merz. UCI Repository of Machine Learning Databases. http://www.ics.uci.edu/mlearn/MLRepository.html, 1998.
  3. [Breiman84]
    L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth & Brooks/Cole Advanced Books & Software, 1984.Google Scholar
  4. [Clark89]
    P. Clark and T. Niblett. The CN2 Induction Algorithm. Machine Learning Vol. 3, pp. 261–283, 1989.Google Scholar
  5. [Cook94]
    D. J. Cook and L. B. Holder. Substructure Discovery Using Minimum Description Length and Background Knowledge. Journal of Artificial Intelligence Research, Vol. 1, pp. 231–255, 1994.Google Scholar
  6. [Inokuchi99]
    A. Inokuchi, T. Washio and H. Motoda. Basket Analysis for Graph Structured Data, Proc. of the Third Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD’99), pp. 420–431, 1999.Google Scholar
  7. [Michalski90]
    R. S. Michalski. Learning Flexible Concepts: Fundamental Ideas and a Method Based on Two-Tiered Representaion. In Machine Learning, An Artificial Intelligence Approach, Vol. III, (eds. Kodrtoff Y. and Michalski T.), pp. 63–102, 1990.Google Scholar
  8. [Muggleton89]
    S. Muggleton and L. de Raedt. Inductive Logic Programming: Theory and Methods. Journal of Logic Programming Vol. 19, No. 20, pp. 629–679, 1994.CrossRefMathSciNetGoogle Scholar
  9. [Quinlan86]
    J. R. Quinlan. Induction of decision trees. Machine Learning, Vol. 1, pp. 81–106, 1986.Google Scholar
  10. [Wallace96]
    C. Wallace, K. B. Korb and H. Dai. Causal Discovery via MML, Proc. of the 13th International Conference on Machine Learning (ICML’96), pp. 516–524, 1996.Google Scholar
  11. [Yoshida95]
    K. Yoshida and H. Motoda. CLIP: Concept Learning from Inference Pattern, Artificial Intelligence, Vol. 75, No. 1, pp. 63–92, 1995.CrossRefGoogle Scholar
  12. [Yoshida97]
    K. Yoshida and H. Motoda. Inductive Inference by Stepwise Pair Extension (in Japanese), Journal of Japanese Society for Artificial Intelligence, Vol. 12, No. 1, pp. 58–67, 1997.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Takashi Matsuda
    • 1
  • Tadashi Horiuchi
    • 1
  • Hiroshi Motoda
    • 1
  • Takashi Washio
    • 1
  1. 1.I.S.I.R.Osaka UniversityOsakaJapan

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