Abstract
Recent advancement of data mining techniques has made it possible to mine from complex structured data. Since structure is represented by proper relations and a graph can easily represent relations, knowledge discovery from graph-structured data (graph mining) poses a general problem for mining from structured data. Some examples amenable to graph mining are finding functional components from their behavior, finding typical web browsing patterns, identifying typical substructures of chemical compounds, finding typical subsequences of DNA and discovering diagnostic rules from patient history records. These are based on finding some typicality from a vast amount of graph-structured data.
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Geamsakul, W., Yoshida, T., Ohara, K., Motoda, H., Yokoi, H., Takabayashi, K.: Constructing a decision tree for graph-structured data and its applications. Fundamenta Informaticae 66(1-2), 131–160 (2005)
Inokuchi, A., Washio, T., Motoda, H.: Complete mining of frequent patterns from graphs: Mining graph data. Machine Learning 50(3), 321–354 (2003)
Inokuchi, A., Washio, T., Motoda, H.: General framework for mining frequent subgraphs from labeled graphs. Fundamenta Informaticae 66(1-2), 53–82 (2005)
Matsuda, T., Motoda, H., Washio, T.: Graph-based induction and its applications. Advanced Engineering Informatics 16(2), 135–143 (2002)
Nguyen, P.C., Ohara, K., Motoda, H., Washio, T.: Cl-gbi: A novel approach for extracting typical patterns from graph-structured data. In: Proceedings of the 9th Pacific-Asia Conference on Knowledge Discovery and Data Mining, pp. 639–649 (2005)
Yoshida, K., Motoda, H.: Clip: Concept learning from inference pattern. Journal of Artificial Intelligence 75(1), 63–92 (1995)
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© 2006 Springer-Verlag Berlin Heidelberg
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Motoda, H. (2006). What Can We Do with Graph-Structured Data? – A Data Mining Perspective. In: Sattar, A., Kang, Bh. (eds) AI 2006: Advances in Artificial Intelligence. AI 2006. Lecture Notes in Computer Science(), vol 4304. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11941439_1
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DOI: https://doi.org/10.1007/11941439_1
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-49787-5
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