An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data

  • Akihiro Inokuchi
  • Takashi Washio
  • Hiroshi Motoda
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1910)


This paper proposes a novel approach named AGM to efficiently mine the association rules among the frequently appearing sub-structures in a given graph data set. A graph transaction is represented by an adjacency matrix, and the frequent patterns appearing in the matrices are mined through the extended algorithm of the basket analysis. Its performance has been evaluated for the artificial simulation data and the carcinogenesis data of Oxford University and NTP. Its high efficiency has been confirmed for the size of a real-world problem....


Normal Form Association Rule Adjacency Matrix Support Threshold Inductive Logic Programming 
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.


  1. 1.
    Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proc. of the 20th VLDB Conference, pp.487–499.Google Scholar
  2. 2.
    Cook, D.J. and Holder, L.B. 1994. Substructure Discovery Using Minimum Description Length and Background Knowledge, Journal of Artificial Intelligence Research, Vol.1, pp.231–255.Google Scholar
  3. 3.
    Dehaspe, L., Toivonen, H. and King, R.D. 1998. Finding frequent substructures in chemical compounds. In Proceedings of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98), pp.30–36.Google Scholar
  4. 4.
    Fortin, S. 1996. The graph isomorphism problem. Technical Report 96-20, University of Alberta, Edomonton, Alberta, Canada.Google Scholar
  5. 5.
    Inokuchi, A., Washio, T. and Motoda, H. 1999. Derivation of the topology structure from massive graph data. Discovery Science: Proceedings of the Second International Conference, DS’99, pp.330–332.Google Scholar
  6. 6.
    Inokuchi, A. 2000. The study on a fast mining method from massive graph structure data. Master thesis (in Japanese), I.S.I.R., Osaka Univ.Google Scholar
  7. 7.
    King, R., Muggleton, S., Srinivasan, A. and Sternberg, M. 1996. Structure-activity relationships derived by machine learning; The use of atoms and their bond connectives to predict mutagenicity by inductive logic programming. In Proceedings of the National Academy of Sciences, Vol.93, pp.438–442.Google Scholar
  8. 8.
    Klopman, G. 1984. Artificial intelligence approach to structure activity studies. J. Amer. Chem. Soc., Vol.106, pp.7315–7321.Google Scholar
  9. 9.
    Klopman, G. 1992. MultiCASE 1. A hierarchical computer automated structure evaluation program, QSAR, Vol.11, pp.176–184.Google Scholar
  10. 10.
    Kramer, S., Pfahringer, B. and Helma, C. 1997. Mining for causes of cancer: Machine learning experiments at various levels of detail. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), pp.223–226.Google Scholar
  11. 11.
    Mannila, H. and Toivonen, H. 1997. Levelwise search and borders of theories in knowledge discovery. Data Mining and Knowledge Discovery, Vol.1, No.3, pp.241–258.Google Scholar
  12. 12.
    Matsuda, T., Horiuchi, T., Motoda, H. and Washio, T. 2000. Extension of Graph-Based Induction for General Graph Structured Data. In Proceedings of the Fourth Pacific-Asia Conference of Knowledge Discovery and Data Mining (PAKDD2000), pp.420–431.Google Scholar
  13. 13.
    Srinisavan, A., King, R.D., Muggleton, S.H. and Sternberg, M.J.E. 1997. The predictive toxicology evaluation challenge. In Proceedings of the Fifteenth International Joint Conference on Artificial Intelligence (IJCAI-97), pp.4–9.Google Scholar
  14. 14.
    Wang, K. and Liu, H. 1997. Schema discovery for semistructured data. In Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), pp.271–274.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2000

Authors and Affiliations

  • Akihiro Inokuchi
    • 1
  • Takashi Washio
    • 1
  • Hiroshi Motoda
    • 1
  1. 1.I.S.I.R.Osaka UniversityOsakaJapan

Personalised recommendations