Mining the optimal class association rule set

Li, Jiuyong and Shen, Hong and Topor, Rodney (2002) Mining the optimal class association rule set. Knowledge-Based Systems, 15 (7). pp. 399-405. ISSN 0950-7051

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Abstract

[Abstract]: We define an optimal class association rule set to be the minimum rule set with the same predictive power of the complete class association rule set. Using this rule set instead of the complete class association rule set we can avoid redundant computation that would otherwise be required for mining predictive association rules and hence improve the efficiency of the mining process significantly. We present an efficient algorithm for mining the optimal class association rule set using an upward closure property of pruning weak rules before they are actually generated. We have implemented the algorithm and our experimental results show that our algorithm generates the optimal class association rule set, whose size is smaller than 1/17 of the complete class association rule set on average, in significantly less rime than generating the complete class association rule set. Our proposed criterion has been shown very effective for pruning weak rules in dense databases.


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Item Type: Article (Commonwealth Reporting Category C)
Refereed: Yes
Item Status: Live Archive
Additional Information: Deposited in accordance with the copyright policy of the publisher.
Depositing User: epEditor USQ
Faculty / Department / School: Historic - Faculty of Sciences - Department of Maths and Computing
Date Deposited: 20 Nov 2007 00:31
Last Modified: 02 Jul 2013 22:54
Uncontrolled Keywords: association rule mining; data mining; class association rule set
Fields of Research (FOR2008): 08 Information and Computing Sciences > 0801 Artificial Intelligence and Image Processing > 080109 Pattern Recognition and Data Mining
Identification Number or DOI: doi: 10.1016/S0950-7051(02)00024-2
URI: http://eprints.usq.edu.au/id/eprint/3548

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