The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Mining fuzzy association rules in databases
Open accessAbstract
Data mining is the discovery of previously unknown, potentially useful and hidden knowledge in databases. In this paper, we concentrate on the discovery of association rules. Many algorithms have been proposed to find association rules in databases with binary attributes. We introduce the fuzzy association rules of the form, 'If X is A then Y is B ', to deal with quantitative attributes. X, Y are set of attributes and A, B are fuzzy sets which describe X and Y respectively. Using the fuzzy set concept, the discovered rules are more understandable to human. Moreover, fuzzy sets handle numerical values better than existing methods because fuzzy sets soften the effect of sharp boundaries.
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Cites
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
Cited by
A Survey of Data Mining and Machine Learning Methods for Cyber Security Intrusion Detection
Buczak, A. L., & Guven, E. (2016) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2015.2494502 · Cited by 3,332
Metadata
- Authors
- Kuok, C. M., Fu, A., & Wong, M. H.
- Venue
- ACM SIGMOD Record
- Published
- 1998-03
- Volume
- 27
- Issue
- 1
- Pages
- 41-46
- ISSN
- 0163-5808 (ISSN-L)
- OpenAlex
- W2036959577
- Cited by
- 530
- References
- 1
- FWCI
- 10.34
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Kuok, C. M., Fu, A., & Wong, M. H. (1998). Mining fuzzy association rules in databases. ACM SIGMOD Record. https://doi.org/10.1145/273244.273257
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
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.
