Abstract
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....
Currently beeing in Tokyo Research Institute, IBM, 1623-14 Shimotsuruma, Yamatoshi, Kanagawa, 242-8502, Japan.
Chapter PDF
Similar content being viewed by others
Keywords
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.
References
Agrawal, R. and Srikant, R. 1994. Fast algorithms for mining association rules. In Proc. of the 20th VLDB Conference, pp.487–499.
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.
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.
Fortin, S. 1996. The graph isomorphism problem. Technical Report 96-20, University of Alberta, Edomonton, Alberta, Canada.
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.
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.
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.
Klopman, G. 1984. Artificial intelligence approach to structure activity studies. J. Amer. Chem. Soc., Vol.106, pp.7315–7321.
Klopman, G. 1992. MultiCASE 1. A hierarchical computer automated structure evaluation program, QSAR, Vol.11, pp.176–184.
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.
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.
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.
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.
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.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2000 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Inokuchi, A., Washio, T., Motoda, H. (2000). An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_2
Download citation
DOI: https://doi.org/10.1007/3-540-45372-5_2
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-41066-9
Online ISBN: 978-3-540-45372-7
eBook Packages: Springer Book Archive