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Association rules mining with relative weighted support

Abdullah, Zailani and Mat Deris, Mustafa Association rules mining with relative weighted support. Proceedings of the 11th International Conference on Information Integration and Web-based Applications & Services.

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Mining weighted association rules are very important in a domain of knowledge discovery. Most of traditional association rules are focused on binary relationships rather than the mixture binary-weight relationships of items. As a result, the significance of weight of each item in a transaction is just ignored completely. Until this instance, only few studies are dedicated for weighted schemes as compared to existing binary association rules. Here, we propose a novel weighted association rules scheme called Relative Weighted Support (RWS). The result reveals that RWS can easily discover the significance of weighted association rules and surprisingly all of them are extracted from the least items.

Item Type: Article
Uncontrolled Keywords: weighted association rules, knowledge discovery, least
Subjects: T Technology > T Technology (General)
Divisions: Faculty of Computer Science and Information Technology > Department of Software Engineering
Depositing User: Normajihan Abd. Rahman
Date Deposited: 28 Mar 2012 00:50
Last Modified: 28 Mar 2012 01:02
URI: http://eprints.uthm.edu.my/id/eprint/2207
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