Abstract
In some real world applications, the data can be represented naturally in a special kind of graphs in which each vertex consists of a set of (structured) data such as item sets, sequences and so on. One of the typical examples is metabolic pathways in bioinformatics. Metabolic pathway is represented in a graph structured data in which each vertex corresponds to an enzyme described by a set of various kinds of properties such as amino acid sequence, enzyme number and so on. We call this kind of complex graphs multi-structured graphs. In this paper, we propose an algorithm named FMG for mining frequent patterns in multi-structured graphs. In FMG, while the external structure will be expanded by the same mechanism of conventional graph miners, the internal structure will be enumerated by the algorithms suitable for its structure. In addition, FMG employs novel pruning techniques to exclude uninteresting patterns. The preliminary experimental results with real datasets show the effectiveness of the proposed algorithm.
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
Borgelt, C.: On Canonical Forms for Frequent Graph Mining. In: Proc. of the 3rd International Workshop on Mining Graphs, pp. 1–12 (2005)
Borgelt, C., Berthold, M.R.: Mining molecular fragments: Finding relevant substructures of molecules. In: Proc. of the 2nd IEEE International Conference on Data Mining, pp. 51–58 (2002)
Chen, Y.L., Kao, H., Ko, M.: Mining DAG Patterns from DAG Databases. In: Li, Q., Wang, G., Feng, L. (eds.) WAIM 2004. LNCS, vol. 3129, pp. 579–588. Springer, Heidelberg (2004)
De Raedt, L., Washio, T., Kok, J.N. (eds.): Advances in Mining Graphs, Trees and Sequences, Frontiers in Artificial Intelligence and Applications, vol. 124. IOS Press, Amsterdam (2005)
Huan, J., Wang, W., Prins, J.: Efficient mining of frequent subgraphs in the presence of isomorphism. In: Proc. of the 3rd IEEE International Conference on Data Mining, pp. 549–552 (2003)
Inokuchi, A., Washio, T., Motoda, H.: An Apriori-Based Algorithm for Mining Frequent Substructures from Graph Data. In: Proc. of the 4th European Conference on Principles and Practice of Knowledge Discovery in Databases, pp. 13–23 (2000)
Inokuchi, A., Washio, T., Motoda, H.: Complete mining of frequent patterns from graphs: Mining graph data. Machine Learning 50, 321–354 (2003)
Knijf, J.D.: FAT-miner: Mining Frequent Attribute Trees. In: Proc. of the 2007 ACM symposium on Applied computing, pp. 417–422 (2007)
Koyuturk, M., Grama, A., Szpankowski, W.: An efficient algorithm for detecting frequent subgraphs in biological networks. Bioinformatics 20, 200–207 (2004)
Kuramochi, M., Karypis, G.: Frequent subgraph discovery. In: Proc. of the 1st IEEE International Conference on Data Mining, pp. 313–320 (2001)
Nijssen, S., Kok, J.N.: A Quickstart in Frequent Structure Mining can make a Difference. In: Proc. of 10th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 647–652 (2004)
Pei, J., Han, J., Mortazavi-Asl, B., Pnto, H., Chen, Q., Dayal, U., Hsu, M.: PrefixSpan: Mining Sequential Patterns Efficiently by Prefix-Projected Pattern Growth. In: Proc. of International Conference on Data Engineering, pp. 215–224 (2001)
Sato, I., Nakagawa, H.: Semi-structure Mining Method for Text Mining with a Chunk-Based Dependency Structure. In: Zhou, Z.-H., Li, H., Yang, Q. (eds.) PAKDD 2007. LNCS (LNAI), vol. 4426, pp. 777–784. Springer, Heidelberg (2007)
Uno, T., Asai, T., Uchida, Y., Arimura, H.: An Efficient Algorithm for Enumerating Closed Patterns in Transaction Databases. In: Proc. of Discovery Science (2004)
Washio, T., Motoda, H.: State of the art of graph-based data mining. SIGKDD Explorations 5(1), 59–68 (2003)
Yan, X., Han, J.: CloseGraph: Mining Closed Frequent Graph Patterns. In: Proc. of the 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 286–295 (2003)
Yan, X., Han, J.: gSpan: Graph-Based Substructure Pattern Mining. In: Proc. of the 2nd IEEE International Conference on Data Mining, pp. 721–724 (2002)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Yamamoto, T., Ozaki, T., Ohkawa, T. (2008). Discovery of Frequent Graph Patterns that Consist of the Vertices with the Complex Structures. In: Raś, Z.W., Tsumoto, S., Zighed, D. (eds) Mining Complex Data. MCD 2007. Lecture Notes in Computer Science(), vol 4944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-68416-9_12
Download citation
DOI: https://doi.org/10.1007/978-3-540-68416-9_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68415-2
Online ISBN: 978-3-540-68416-9
eBook Packages: Computer ScienceComputer Science (R0)