Please use this identifier to cite or link to this item:
https://scholarbank.nus.edu.sg/handle/10635/14013
DC Field | Value | |
---|---|---|
dc.title | Mining of gradual rules | |
dc.contributor.author | CHEN ZHENG | |
dc.date.accessioned | 2010-04-08T10:39:00Z | |
dc.date.available | 2010-04-08T10:39:00Z | |
dc.date.issued | 2004-06-28 | |
dc.identifier.citation | CHEN ZHENG (2004-06-28). Mining of gradual rules. ScholarBank@NUS Repository. | |
dc.identifier.uri | http://scholarbank.nus.edu.sg/handle/10635/14013 | |
dc.description.abstract | Previous research shows that associative classification methods could generate thousands rules and make it hard for the user to manually inspect the rules. On the other hand, discovering change pattern in dataset is an important topic in data mining research. Many existing algorithms typically assume that the underlying rules are stable, however, in real world domain, it is possible that the data characteristic is dynamic and undesirable change in the dataset may not even be limited to the time dimension. In this thesis, we integrate the CBA with the fuzzy gradual rule mining approach proposed by previous research and build the classifier from the dataset. By mining gradual rules, this method can represent how data change and the number of rules is reduced significantly compared with traditional associative classifiers. The implementation on UCI dataset and real-life dataset are described, experiment results show that the fuzzy rules generated are more accurate. | |
dc.language.iso | en | |
dc.subject | Data Mining, Fuzzification, Fuzzy Gradual Rules, Fuzzy Association, Knowledge Discovery, Apriori Algorithm | |
dc.type | Thesis | |
dc.contributor.department | COMPUTER SCIENCE | |
dc.contributor.supervisor | HSU, WYNNE | |
dc.description.degree | Master's | |
dc.description.degreeconferred | MASTER OF SCIENCE | |
dc.identifier.isiut | NOT_IN_WOS | |
Appears in Collections: | Master's Theses (Open) |
Show simple item record
Files in This Item:
File | Description | Size | Format | Access Settings | Version | |
---|---|---|---|---|---|---|
thesis_ack.pdf | 58.98 kB | Adobe PDF | OPEN | None | View/Download | |
Thesis_cover1.pdf | 13.38 kB | Adobe PDF | OPEN | None | View/Download | |
Thesis_cover2.pdf | 20.22 kB | Adobe PDF | OPEN | None | View/Download | |
thesis_content.pdf | 513.77 kB | Adobe PDF | OPEN | None | View/Download |
Google ScholarTM
Check
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.