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CloseGraph: mining closed frequent graph patterns
Abstract
Recent research on pattern discovery has progressed form mining frequent itemsets and sequences to mining structured patterns including trees, lattices, and graphs. As a general data structure, graph can model complicated relations among data with wide applications in bioinformatics, Web exploration, and etc. However, mining large graph patterns in challenging due to the presence of an exponential number of frequent subgraphs. Instead of mining all the subgraphs, we propose to mine closed frequent graph patterns. A graph g is closed in a database if there exists no proper supergraph of g that has the same support as g. A closed graph pattern mining algorithm, CloseGraph, is developed by exploring several interesting pruning methods. Our performance study shows that CloseGraph not only dramatically reduces unnecessary subgraphs to be generated but also substantially increases the efficiency of mining, especially in the presence of large graph patterns.
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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
Self-citation
Cited by
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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
Self-citationGPLAG: detection of software plagiarism by program dependence graph analysis
Liu, C., Chen, C., Han, J., & Yu, P. S. (2006) · Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/1150402.1150522 · Cited by 422
Self-citationCatchSync: catching synchronized behavior in large directed graphs
Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S. (2014) · Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2623330.2623632 · Cited by 151
Catching Synchronized Behaviors in Large Networks: A Graph Mining Approach
Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S. (2016) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2746403 · Cited by 88
Inferring lockstep behavior from connectivity pattern in large graphs
Jiang, M., Cui, P., Beutel, A., Faloutsos, C., & Yang, S. (2016) · Knowledge and Information Systems · doi:10.1007/s10115-015-0883-y · Cited by 43
Metadata
- Authors
- Yan, X., & Han, J.
- Venue
- Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2003-08-24
- Pages
- 286-295
- OpenAlex
- W2108150886
- Cited by
- 635
- References
- 1
- FWCI
- 34.39
- Type
- conference paper
- Language
- en
- Open access
- closed
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