Mining the optimal class association rule set

Article

Li, Jiuyong, Shen, Hong and Topor, Rodney. 2002. "Mining the optimal class association rule set." Knowledge-Based Systems. 15 (7), pp. 399-405. https://doi.org/10.1016/S0950-7051(02)00024-2
Article Title

Mining the optimal class association rule set

ERA Journal ID18062
Article CategoryArticle
AuthorsLi, Jiuyong (Author), Shen, Hong (Author) and Topor, Rodney (Author)
Journal TitleKnowledge-Based Systems
Journal Citation15 (7), pp. 399-405
Year2002
PublisherElsevier
Place of PublicationNetherlands
ISSN0950-7051
1872-7409
Digital Object Identifier (DOI)https://doi.org/10.1016/S0950-7051(02)00024-2
Web Address (URL)http://www.sciencedirect.com/science/journal/09507051
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.

Keywordsassociation rule mining; data mining; class association rule set
ANZSRC Field of Research 2020469999. Other information and computing sciences not elsewhere classified
Public Notes

File reproduced in accordance with the copyright policy of the publisher/author.

Byline AffiliationsDepartment of Mathematics and Computing
Japan Advanced Institute of Science and Technology, Japan
Griffith University
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