Abstract
Free tree, as a special graph which is connected, undirected and acyclic, has been extensively used in bioinformatics, pattern recognition, computer networks, XML databases, etc. Recent research on structural pattern mining has focused on an important problem of discovering frequent free trees in large graph databases. However, it can be prohibitive due to the presence of an exponential number of frequent free trees in the graph database. In this paper, we propose a computationally efficient algorithm that discovers only closed frequent free trees in a database of labeled graphs. A free tree t is closed if there exist no supertrees of t that has the same frequency of t. Two pruning algorithms, the safe position pruning and the safe label pruning, are proposed to efficiently detect unsatisfactory search spaces with no closed frequent free trees generated. Based on the special characteristics of free tree, the automorphism-based pruning and the canonical mapping-based pruning are introduced to facilitate the mining process. Our performance study shows that our algorithm not only reduces the number of false positives generated but also improves the mining efficiency, especially in the presence of large frequent free tree patterns in the graph database.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Chi, Y., Xia, Y., Yang, Y., Muntz, R.R.: Mining closed and maximal frequent subtrees from databases of labeled rooted trees. IEEE Transactions on Knowledge and Data Engineering 17(2), 190–202 (2005)
Chi, Y., Yang, Y., Muntz, R.R.: Indexing and mining free trees. In: Proceedings of ICDM03 (2003)
Garey, M.R., Johnson, D.S.: Computers and Intractability: A Guide to the Theory of NP-Completeness (1979)
Huan, J., Wang, W., Prins, J.: Efficient mining of frequent subgraphs in the presence of isomorphism. In: Proceedings of ICDM03 (2003)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proceedings of ICDM01 (2001)
Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: Proceedings of KDD04 (2004)
Pasquier, N., Bastide, Y., Taouil, R., Lakhal, L.: Discovering Frequent Closed Itemsets for Association Rules. In: Beeri, C., Bruneman, P. (eds.) ICDT 1999. LNCS, vol. 1540, pp. 398–416. Springer, Heidelberg (1998)
Rückert, U., Kramer, S.: Frequent free tree discovery in graph data. In: Proceedings of SAC04 (2004)
Yan, X., Han, J.: gspan: Graph-based substructure pattern mining. In: Proceedings of ICDM02 (2002)
Yan, X., Han, J.: Closegraph: mining closed frequent graph patterns. In: Proceedings of KDD03 (2003)
Yan, X., Han, J., Afshar, R.: Clospan: Mining closed sequential patterns in large databases. In: Proceedings of SDM03 (2003)
Zhao, P., Yu, J.X.: Fast frequent free tree mining in graph databases. In: Proceedings of MCD06 - ICDM 2006 Workshop, Hong Kong, China (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2007 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Zhao, P., Yu, J.X. (2007). Mining Closed Frequent Free Trees in Graph Databases. In: Kotagiri, R., Krishna, P.R., Mohania, M., Nantajeewarawat, E. (eds) Advances in Databases: Concepts, Systems and Applications. DASFAA 2007. Lecture Notes in Computer Science, vol 4443. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71703-4_10
Download citation
DOI: https://doi.org/10.1007/978-3-540-71703-4_10
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-71702-7
Online ISBN: 978-3-540-71703-4
eBook Packages: Computer ScienceComputer Science (R0)