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Frequent subgraph discovery
Abstract
As data mining techniques are being increasingly applied to non-traditional domains, existing approaches for finding frequent itemsets cannot be used as they cannot model the requirement of these domains. An alternate way of modeling the objects in these data sets is to use graphs. Within that model, the problem of finding frequent patterns becomes that of discovering subgraphs that occur frequently over the entire set of graphs.The authors present a computationally efficient algorithm for finding all frequent subgraphs in large graph databases. We evaluated the performance of the algorithm by experiments with synthetic datasets as well as a chemical compound dataset. The empirical results show that our algorithm scales linearly with the number of input transactions and it is able to discover frequent subgraphs from a set of graph transactions reasonably fast, even though we have to deal with computationally hard problems such as canonical labeling of graphs and subgraph isomorphism which are not necessary for traditional frequent itemset discovery.
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Cites
Mining frequent patterns without candidate generation
Han, J., Pei, J., & Yin, Y. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335372 · Cited by 6,402
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Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504
Frequent pattern mining: current status and future directions
Han, J., Cheng, H., Xin, D., & Yan, X. (2007) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-006-0059-1 · Cited by 1,380
Discriminative Frequent Pattern Analysis for Effective Classification
Cheng, H., Yan, X., Han, J., & Hsu, C.-W. (2007) · 2007 IEEE 23rd International Conference on Data Engineering · doi:10.1109/icde.2007.367917 · Cited by 357
Top-Down Synthesis for Library Learning
Bowers, M., Olausson, T. X., Wong, L., Grand, G., Tenenbaum, J. B., Ellis, K., & Solar-Lezama, A. (2023) · Proceedings of the ACM on Programming Languages · doi:10.1145/3571234 · Cited by 33
Metadata
- Authors
- Kuramochi, M., & Karypis, G.
- Venue
- Proceedings 2001 IEEE International Conference on Data Mining
- Publisher
- IEEE Comput. Soc
- Published
- 2002-11-14
- Pages
- 313-320
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2136593687
- Cited by
- 1,068
- References
- 1
- FWCI
- 38.02
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Kuramochi, M., & Karypis, G. (2002). Frequent subgraph discovery. Proceedings 2001 IEEE International Conference on Data Mining. https://doi.org/10.1109/icdm.2001.989534
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