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Efficient mining of frequent subgraphs in the presence of isomorphism
Abstract
Frequent subgraph mining is an active research topic in the data mining community. A graph is a general model to represent data and has been used in many domains like cheminformatics and bioinformatics. Mining patterns from graph databases is challenging since graph related operations, such as subgraph testing, generally have higher time complexity than the corresponding operations on itemsets, sequences, and trees, which have been studied extensively. We propose a novel frequent subgraph mining algorithm: FFSM, which employs a vertical search scheme within an algebraic graph framework we have developed to reduce the number of redundant candidates proposed. Our empirical study on synthetic and real datasets demonstrates that FFSM achieves a substantial performance gain over the current start-of-the-art subgraph mining algorithm gSpan.
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Cited by
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
Lin, W., Ma, M., Pan, D., & Wang, P. (2018) · 2018 IEEE 37th International Performance Computing and Communications Conference (IPCCC) · doi:10.1109/pccc.2018.8711092 · Cited by 29
Metadata
- Authors
- Huan, J., Wang, W., & Prins, J.
- Venue
- Third IEEE International Conference on Data Mining
- Publisher
- IEEE Comput. Soc
- Published
- 2004-04-23
- Pages
- 549-552
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2118349699
- Cited by
- 614
- References
- 0
- FWCI
- 25.54
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Huan, J., Wang, W., & Prins, J. (2004). Efficient mining of frequent subgraphs in the presence of isomorphism. Third IEEE International Conference on Data Mining. https://doi.org/10.1109/icdm.2003.1250974
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
