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. Improvement is made on its search capability without imposing much computational complexity by 1) incorporating a beam search, 2) using a different evaluation function to extract patterns that are more discriminatory than those simply occurring frequently, and 3) adopting canonical labeling to enumerate identical patterns accurately. This new algorithm, now called Beam-wise GBI, B-GBI for short, was tested against a small DNA dataset from UCI repository and shown successful in extracting discriminatory substructures.
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References
C. L. Blake, E. Keogh, and C. J. Merz. Uci repository of machine leaning database, 1998. http://www.ics.uci.edu/∼mlearn/MLRepository.html.
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. Classification and Regression Trees. Wadsworth & Brooks/Cole Advanced Books & Software, 1984.
L. Breiman, J. H. Friedman, R. A. Olshen, and C. J. Stone. The cn2 induction algorithm. Machine Learning, 3:261–283, 1989.
D. J. Cook and L. B. Holder. Graph-based data mining. IEEE Intelligent Systems, 15(2):32–41, 2000.
L. De Raedt and S. Kramer. The levelwise version space algorithm and its application to molecular fragment finding. In Proc. the 17th International Joint Conference on Artificial Intelligence, pages 853–859, 2001.
L. Dehaspe, H. Toivonen, and R. D. King. Finding frequent substructures in chemical compound. In Proc. the 4th International conference on Knowledge Discovery and Data Mining, pages 30–36, 1998.
S. Fortin. The graph isomorphism problem, 1996.
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.
S. Kramer, L. De Raedt, and C. Helma. Molecular feature miing in hiv data. In Proc. the 7th ACM SIGKDD International conference on Knowledge Discovery and Data Mining, pages 136–143, 2001.
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.
T. Matsuda, H. Motoda, and T. Washio. Graph-based induction and its applications. Advanced Engineering Informatics, 16(2):135–143, 2002.
R. S. Michalski. Learning fiexible concepts: Fundamental ideas and a method based on two-tiered representaion. In Machine Learning, An Artificial Intelligence Approiach, 3:63–102, 1990.
S. Muggleton and L. de Raedt. Inductive logic programming: Theory and methods. Journal of Logic Programming, 19(20):629–679, 1994.
J. R. Quinlan. Induction of decision trees. Machine Learning, 1:81–106, 1986.
J. R. Quinlan. C4.5:Programs For Machine Learning. Morgan Kaufmann Publishers, 1993.
R. C. Read and D. G. Corneil. The graph isomorphism disease. Journal of Graph Theory, 1:339–363, 1977.
G. G. Towell and J. W. Shavlik. Extracting refined rules from knowledge-based neural networks. Machine Learning, 13:71–101, 1993.
K. Yoshida and H. Motoda. Clip: Concept learning from inference pattern. Journal of Artificial Intelligence, 75(1):63–92, 1995.
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Matsuda, T., Motoda, H., Yoshida, T., Washio, T. (2002). Mining Patterns from Structured Data by Beam-Wise Graph-Based Induction. In: Lange, S., Satoh, K., Smith, C.H. (eds) Discovery Science. DS 2002. Lecture Notes in Computer Science, vol 2534. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-36182-0_44
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DOI: https://doi.org/10.1007/3-540-36182-0_44
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