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Knowledge Discovery from Structured Data by Beam-Wise Graph-Based Induction

  • Takashi Matsuda
  • Hiroshi Motoda
  • Tetsuya Yoshida
  • Takashi Washio
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2417)

Abstract

A machine learning technique called Graph-Based Induction (GBI) extracts typical patterns from graph data by stepwise pair expansion (pairwise chunking). Because of its greedy search strategy, it is very efficient but suffers from incompleteness of search. We improved its search capability without imposing much computational complexity by incorporating the idea of beam search. Additional improvement is made to extract patterns that are more discriminative than those simply occurring frequently, and to enumerate identical patterns accurately based on the notion of canonical labeling. This new algorithm was implemented (now called Beam-wise GBI, B-GBI for short) and tested against a DNA data set from UCI repository. Since DNA data is a sequence of symbols, representing each sequence by attribute-value pairs by simply assigning these symbols to the values of ordered attributes does not make sense. By transforming the sequence into a graph structure and running B-GBI it is possible to extract discriminative substructures. These can be new attributes for a classification problem. Effect of beam width on the number of discovered attributes and predictive accuracy was evaluated, together with extracted characteristic subsequences, and the results indicate the effectiveness of B-GBI.

Keywords

Typical Pattern Beam Width Input Graph Inductive Logic Programming Greedy Search 
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.

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References

  1. 1.
    C. L. Blake, E. Keogh, and C.J. Merz. Uci repository of machine leaning database, 1998. http://www.ics.uci.edu/~mlearn/MLRepository.html.
  2. 2.
    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
  3. 3.
    L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. The cn2 induction algorithm. Machine Learning, 3:261–283, 1989.Google Scholar
  4. 4.
    D. J. Cook and L. B. Holder. Graph-based data mining. IEEE Intelligent Systems, 15(2):32–41, 2000.CrossRefGoogle Scholar
  5. 5.
    S. Fortin. The graph isomorphism problem, 1996.Google Scholar
  6. 6.
    A. Inokuchi, T. Washio, and H. Motoda. An apriori-based algorithm for mining frequent substructures from graph data. In Proc. of the 4th European Conference on Principles of Data Mining and Knowledge Discovery, pages 13–23, 2000.Google Scholar
  7. 7.
    T. Matsuda, T. Horiuchi, H. Motoda, and T. Washio. Extension of graph-based induction for general graph structured data. In Knowledge Discovery and Data Mining: Current Issues and New Applications, Springer Verlag, LNAI 1805, pages 420–431, 2000.Google Scholar
  8. 8.
    R. S. Michalski. Learning flexible concepts: Fundamental ideas and a method based on two-tiered representaion. In Machine Learning, An Artificial Intelligence Approiach, 3:63–102, 1990.Google Scholar
  9. 9.
    S. Muggleton and L. de Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19(20):629–679, 1994.MathSciNetCrossRefGoogle Scholar
  10. 10.
    J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986.Google Scholar
  11. 11.
    J. R. Quinlan. C4.5:Programs For Machine Learning. Morgan Kaufmann Publishers, 1993.Google Scholar
  12. 12.
    R. C. Read and D. G. Corneil. The graph isomorphism disease. Journal of Graph Theory, 1:339–363, 1977.zbMATHMathSciNetCrossRefGoogle Scholar
  13. 13.
    K. Yoshida and H. Motoda. Clip: Concept learning from inference pattern. Journal of Artificial Intelligence, 75(1):63–92, 1995.CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2002

Authors and Affiliations

  • Takashi Matsuda
    • 1
  • Hiroshi Motoda
    • 1
  • Tetsuya Yoshida
    • 1
  • Takashi Washio
    • 1
  1. 1.Institute of Scientific and Industrial ResearchOsaka UniversityOsakaJapan

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