The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
State of the art of graph-based data mining
Abstract
The need for mining structured data has increased in the past few years. One of the best studied data structures in computer science and discrete mathematics are graphs. It can therefore be no surprise that graph based data mining has become quite popular in the last few years.This article introduces the theoretical basis of graph based data mining and surveys the state of the art of graph-based data mining. Brief descriptions of some representative approaches are provided as well.
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Cites
The Nature of Statistical Learning Theory
Vapnik, V. N. (1995) · doi:10.1007/978-1-4757-2440-0 · Cited by 39,138
Debnath, A. K., Lopez de Compadre, R. L., Debnath, G., Shusterman, A. J., & Hansch, C. (1991) · Journal of Medicinal Chemistry · doi:10.1021/jm00106a046 · Cited by 868
Cited by
Schaeffer, S. E. (2007) · Computer Science Review · doi:10.1016/j.cosrev.2007.05.001 · Cited by 1,639
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
Metadata
- Authors
- Washio, T., & Motoda, H.
- Venue
- ACM SIGKDD Explorations Newsletter
- Published
- 2003-07
- Volume
- 5
- Issue
- 1
- Pages
- 59-68
- ISSN
- 1931-0145 (ISSN-L)
- 1931-0153
- OpenAlex
- W2130426318
- Cited by
- 448
- References
- 2
- FWCI
- 26.59
- Type
- journal article
- Language
- en
- Open access
- closed
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- Washio, T., & Motoda, H. (2003). State of the art of graph-based data mining. ACM SIGKDD Explorations Newsletter. https://doi.org/10.1145/959242.959249
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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.
