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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Ying Liu Xuehua Wang |
| Copyright Year | 2010 |
| Abstract | It is an objective fact that large database has inconsistent data. This paper presents a new rule acquisition method based on rough set theory and genetic algorithm. Using rough set theory, we will divide inconsistent data table into two parts, certain data and possible data, and then standard genetic algorithm is used for mining rules set. When the algorithm is processing, the user is allowed to set three evaluation parameter values of the rules: support, confidence, coverage for specific application needs. This algorithm will delete the rules which do not meet the requirements, so we can reduce the amount of data in the case of massive data. The advantage of this setting is obvious. Finally, we use an case to verify this method. |
| Starting Page | 1 |
| Ending Page | 4 |
| File Size | 240421 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424471591 |
| e-ISBN | 9781424471614 |
| DOI | 10.1109/ICEEE.2010.5660849 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-11-07 |
| Publisher Place | China |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Search engines Set theory Genetics Classification algorithms Approximation methods Biological cells Information systems |
| Content Type | Text |
| Resource Type | Article |
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