Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/14013
Title: Mining of gradual rules
Authors: CHEN ZHENG
Keywords: Data Mining, Fuzzification, Fuzzy Gradual Rules, Fuzzy Association, Knowledge Discovery, Apriori Algorithm
Issue Date: 28-Jun-2004
Source: CHEN ZHENG (2004-06-28). Mining of gradual rules. ScholarBank@NUS Repository.
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.
URI: http://scholarbank.nus.edu.sg/handle/10635/14013
Appears in Collections:Master's Theses (Open)

Show full item record
Files in This Item:
File Description SizeFormatAccess SettingsVersion 
thesis_ack.pdf58.98 kBAdobe PDF

OPEN

NoneView/Download
Thesis_cover1.pdf13.38 kBAdobe PDF

OPEN

NoneView/Download
Thesis_cover2.pdf20.22 kBAdobe PDF

OPEN

NoneView/Download
thesis_content.pdf513.77 kBAdobe PDF

OPEN

NoneView/Download

Page view(s)

163
checked on Dec 11, 2017

Download(s)

415
checked on Dec 11, 2017

Google ScholarTM

Check


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.