A new algorithm for mining fuzzy association rules

Gao, Y; Ma, J; Ma, L

A new algorithm for mining fuzzy association rules

Gao, Y Ma, J Ma, L

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Publisher:: IEEE
Publication Type:: Conference Proceeding
Citation:: Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 3 pp. 1635 - 1640
Issue Date:: 2004-01

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Field	Value	Language
dc.contributor.author	Gao, Y	en_US
dc.contributor.author	Ma, J	en_US
dc.contributor.author	Ma, L	en_US
dc.contributor.editor	NA	en_US
dc.date	2004-08-26	en_US
dc.date.issued	2004-01	en_US
dc.identifier.citation	Proceedings of 2004 International Conference on Machine Learning and Cybernetics, 2004, 3 pp. 1635 - 1640	en_US
dc.identifier.isbn	0-7803-8403-2	en_US
dc.identifier.uri	http://hdl.handle.net/10453/11903
dc.description.abstract	We introduce a new algorithm for mining the fuzzy association rules by removing redundant fuzzy association (RFA) rules. Firstly, we analyze some properties of fuzzy association rules and give the definition of RFA rules. Secondly, using the degree of implication on fuzzy implication operator, we introduce a new algorithm to mine fuzzy association rules from frequent itemsets. Finally, an example is given to illustrate our idea.	en_US
dc.publisher	IEEE	en_US
dc.relation.ispartof	Proceedings of 2004 International Conference on Machine Learning and Cybernetics	en_US
dc.relation.ispartof	International Conference on Machine Learning and Cybernetics	en_US
dc.relation.isbasedon	10.1109/ICMLC.2004.1382037	en_US
dc.rights	© 2004 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.	en_US
dc.title	A new algorithm for mining fuzzy association rules	en_US
dc.type	Conference Proceeding
utslib.citation.volume	3	en_US
utslib.location	Piscataway, USA	en_US
utslib.location.activity	Shanghai, China	en_US
utslib.for	0801 Artificial Intelligence and Image Processing	en_US
dc.location.activity	Shanghai, China	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
utslib.copyright.status	closed_access
pubs.consider-herdc	false	en_US
pubs.place-of-publication	Piscataway, USA	en_US
pubs.start-date	2004-08-26	en_US
pubs.volume	3	en_US

Abstract:

We introduce a new algorithm for mining the fuzzy association rules by removing redundant fuzzy association (RFA) rules. Firstly, we analyze some properties of fuzzy association rules and give the definition of RFA rules. Secondly, using the degree of implication on fuzzy implication operator, we introduce a new algorithm to mine fuzzy association rules from frequent itemsets. Finally, an example is given to illustrate our idea.

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http://hdl.handle.net/10453/11903