Mining the optimal class association rule set
Article
Article Title | Mining the optimal class association rule set |
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ERA Journal ID | 18062 |
Article Category | Article |
Authors | Li, Jiuyong (Author), Shen, Hong (Author) and Topor, Rodney (Author) |
Journal Title | Knowledge-Based Systems |
Journal Citation | 15 (7), pp. 399-405 |
Year | 2002 |
Publisher | Elsevier |
Place of Publication | Netherlands |
ISSN | 0950-7051 |
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. |
Keywords | association rule mining; data mining; class association rule set |
ANZSRC Field of Research 2020 | 469999. Other information and computing sciences not elsewhere classified |
Public Notes | File reproduced in accordance with the copyright policy of the publisher/author. |
Byline Affiliations | Department of Mathematics and Computing |
Japan Advanced Institute of Science and Technology, Japan | |
Griffith University |
https://research.usq.edu.au/item/9y7yw/mining-the-optimal-class-association-rule-set
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