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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chun-Hao Chen Ai-Fang Li Yeong-Chyi Lee Tzung-Pei Hong |
| Copyright Year | 2012 |
| Description | Author affiliation: Department of Computer Science and Information Engineering, Tamkang University, Taipei, Taiwan, R.O.C. (Chun-Hao Chen; Ai-Fang Li) || Department of Science and Information Engineering, National University of Kaohsiung, 811, Taiwan, R.O.C. (Tzung-Pei Hong) || Department of Information Management, Cheng Shiu University, Kaohsiung, Taiwan, R. O. C. (Yeong-Chyi Lee) |
| Abstract | Many fuzzy data mining approaches have been proposed for finding fuzzy association rules with the predefined minimum support from the give quantitative transactions. However, some comment problems of those approaches are that (1) a minimum support should be predefined, and it is hard to set the appropriate one, and (2) the derived rules usually expose common-sense knowledge which may not be interested in business point of view. In this paper, we thus proposed an algorithm for mining fuzzy coherent rules to overcome those problems with the properties of propositional logic. It first transforms quantitative transactions into fuzzy sets. Then, those generated fuzzy sets are collected to generate candidate fuzzy coherent rules. Finally, contingency tables are calculated and used for checking those candidate fuzzy coherent rules satisfy four criteria or not. Experiments on the foodmart dataset are also made to show the effectiveness of the proposed algorithm. |
| Starting Page | 1 |
| Ending Page | 5 |
| File Size | 924634 |
| Page Count | 5 |
| File Format | |
| ISBN | 9781467315074 |
| ISSN | 10987584 |
| e-ISBN | 9781467315067 |
| e-ISBN | 9781467315050 |
| DOI | 10.1109/FUZZ-IEEE.2012.6251309 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2012-06-10 |
| Publisher Place | Australia |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Association rules Itemsets Fuzzy sets Transforms Conferences data mining fuzzy set fuzzy association rules fuzzy coherent rules membership function |
| Content Type | Text |
| Resource Type | Article |
| Subject | Applied Mathematics Artificial Intelligence Theoretical Computer Science Software |
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