Abstract
The mining of a complete set of frequent subgraphs from labeled graph data has been studied extensively. Recently, much attention has been given to frequent pattern mining from graph sequences. In this paper, we propose a method to improve GTRACE which mines frequent patterns called FTSs (Frequent Transformation Subsequences) from graph sequences. Our performance study shows that the proposed method is efficient and scalable for mining both long and large graph sequence patterns, and is some orders of magnitude faster than the conventional method.
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References
Berlingerio, M., et al.: Mining Graph Evolution Rules. In: Proc. of Euro. Conf. on Principles and Practice of Knowledge Discovery in Databases, pp. 115–130 (2009)
Borgwardt, K.M., et al.: Pattern Mining in Frequent Dynamic Subgraphs. In: Proc. of Int’l Conf. on Data Mining, pp. 818–822 (2006)
Enron Email Dataset, http://www.cs.cmu.edu/~enron/
Garey, M., Johnson, D.: Computers and Intractability: A Guide to the Theory of NP-Completeness. W.H. Freeman, New York (1979)
Inokuchi, A., et al.: An Apriori-based Algorithm for Mining Frequent Substructures from Graph Data. In: Zighed, D.A., Komorowski, J., Żytkow, J.M. (eds.) PKDD 2000. LNCS (LNAI), vol. 1910, pp. 13–23. Springer, Heidelberg (2000)
Inokuchi, A., Washio, T.: A Fast Method to Mine Frequent Subsequences from Graph Sequence Data. In: Proc. of Int’l Conf. on Data Mining, pp. 303–312 (2008)
Inokuchi, A., Washio, T.: Mining Frequent Graph Sequence Patterns Induced by Vertices. In: Proc. of SIAM Int’l Conf. on Data Mining (2010)
Inokuchi, A., et al.: A Fast Algorithm for Mining Frequent Connected Subgraphs. IBM Research Report, RT0448 (2002)
Nijssen, S., Kok, J.N.: A Quickstart in Frequent Structure Mining can Make a Difference. In: Proc. of Int’l Conf. on Knowledge Discovery and Data Mining, pp. 647–652 (2004)
Pei, J., et al.: PrefixSpan: Mining Sequential Patterns by Prefix-Projected Growth. In: Proc. of Int’l Conf. on Data Eng., pp. 2–6 (2001)
Srikant, R., Agrawal, R.: Mining Sequential Patterns: Generalizations and Performance Improvements. In: Proc. of Int’l Conf. on Extending Database Technology, pp. 3–17 (1996)
Yan, X., Han, J.: gSpan: Graph-Based Substructure Pattern Mining. In: Proc. of Int’l Conf. on Data Mining, pp. 721–724 (2002)
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Inokuchi, A., Washio, T. (2010). GTRACE2: Improving Performance Using Labeled Union Graphs. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2010. Lecture Notes in Computer Science(), vol 6119. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13672-6_18
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DOI: https://doi.org/10.1007/978-3-642-13672-6_18
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13671-9
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