Abstract
Mining weighted frequent sub-graphs in graph databases is possible to obtain more complex and various patterns compared with finding patterns in transactional databases, and the gained sub-graphs reflect object’s charateristics in the real world due to weight conditions. However, all of the patterns do not mean really valid patterns. Though any sub-graph is frequent, supports or weights of each element composing the sub-graph can be sharply different, where the graph is more likley to be a meaningless pattern. To solve the problem, we propose novel techniques for mining only meaningful sub-graphs by applying both weight and support affinities to graph mining and a corresponding algorithm, MWSA. Through MWSA, we can effectively eliminate invalid patterns with large gaps among the pattern’s elements. MWSA not only can gain valid sub-graphs but also can improve mining efficiency such as runtime and memory-usage by pruning needless patterns. These advantages are presented through various experiments.
This research was supported by the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (NRF No. 2012-0003740 and 2012-0000478).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bogdanov, P., Mongiovi, M., Singh, A.K.: Mining Heavy Subgraphs in Time-Evolving Networks. In: ICDM, pp. 81–90 (2011)
Bifet, A., Holmes, G., Pfahringer, B., Gavalda, R.: Mining Frequent Closed Graphs on Evolving Data Streams. In: KDD, pp. 591–599 (2011)
Günnemann, S., Seidl, T.: Subgraph Mining on Directed and Weighted Graphs. In: Zaki, M.J., Yu, J.X., Ravindran, B., Pudi, V. (eds.) PAKDD 2010. LNCS, vol. 6119, pp. 133–146. Springer, Heidelberg (2010)
Jiang, C., Coenen, F., Zito, M.: Frequent Sub-graph Mining on Edge Weighted Graphs. In: Bach Pedersen, T., Mohania, M.K., Tjoa, A.M. (eds.) DaWaK 2010. LNCS, vol. 6263, pp. 77–88. Springer, Heidelberg (2010)
Jia, Y., Zhang, J., Huan, J.: An efficient graph-mining method for complicated and noisy data with real-world applications. Knowl. Inf. Syst. 28(2), 423–447 (2011)
Nowozin, S., Tsuda, K., Uno, T., Kudo, T., Bakir, G.H.: Weighted Substructure Mining for Image Analysis. In: CVPR (2007)
Nijssen, S., Kok, J.N.: The Gaston Tool for Frequent Subgraph Mining. Electr. Notes Theor. Comput. Sci. 127(1), 77–87 (2005)
Nijssen, S., Kok, J.N.: A quickstart in frequent structure mining can make a difference. In: KDD, pp. 647–652 (2004)
Ozaki, T., Etoh, M.: Closed and Maximal Subgraph Mining in Internally and Externally Weighted Graph Databases. In: AINA Workshops, pp. 626–631 (2011)
Silva, A., Meira Jr., W., Zaki, M.J.: Mining Attribute-structure Correlated Patterns in Large Attributed Graphs. PVLDB 5(5), 466–477 (2012)
Saigo, H., Nowozin, S., Kadowaki, T., Kudo, T., Tsuda, K.: gBoost: a mathematical programming approach to graph classification and regression. Machine Learning 75(1), 69–89 (2009)
Shang, H., Zhang, Y., Lin, X., Yu, J.X.: Taming verification hardness: an efficient algorithm for testing subgraph isomorphism. PVLDB 1(1), 364–375 (2008)
Xiong, H., Tan, P.N., Kumar, V.: Mining Strong Affinity Association Patterns in Data Sets with Skewed Support Distribution. In: ICDM, pp. 387–394 (2003)
Yun, U.: WIS: Weighted Interesting Sequential Pattern Mining with a Similar Level of Support and/or Weight. ETRI Journal 29(3), 336–352 (2007)
Zhou, Y., Cheng, H., Yu, J.X.: Clustering Large Attributed Graphs: An Efficient Incremental Approach. In: ICDM, pp. 689–698 (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Lee, G., Yun, U. (2012). Mining Weighted Frequent Sub-graphs with Weight and Support Affinities. In: Sombattheera, C., Loi, N.K., Wankar, R., Quan, T. (eds) Multi-disciplinary Trends in Artificial Intelligence. MIWAI 2012. Lecture Notes in Computer Science(), vol 7694. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-35455-7_21
Download citation
DOI: https://doi.org/10.1007/978-3-642-35455-7_21
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-35454-0
Online ISBN: 978-3-642-35455-7
eBook Packages: Computer ScienceComputer Science (R0)