Mining for Useful Association Rules Using the ATMS

Xu, Yue & Li, Yuefeng (2006) Mining for Useful Association Rules Using the ATMS. In Mohammadian, M. (Ed.) International Conference on Computational Intelligence for Modelling, Control and Automation, 28-30 November, Vienna, Austria.

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Association rule mining has made many achievements in the area of knowledge discovery in databases. Recent years, the quality of the extracted association rules has drawn more and more attention from researchers in data mining community. One big concern is with the size of the extracted rule set. Very often tens of thousands of association rules are extracted among which many are redundant thus useless. In this paper, we first analyze the redundancy problem in association rules and then propose a novel ATMS-based method for extracting non-redundant association rules.

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ID Code: 10103
Item Type: Conference Paper
Refereed: Yes
DOI: 10.1109/CIMCA.2005.1631278
ISBN: 9780769525044
Divisions: Past > QUT Faculties & Divisions > Faculty of Science and Technology
Copyright Owner: Copyright 2006 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 12 Oct 2007 00:00
Last Modified: 29 Feb 2012 13:12

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