Journal
EXPERT SYSTEMS WITH APPLICATIONS
Volume 36, Issue 2, Pages 3066-3076Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.eswa.2008.01.028
Keywords
Data mining; Association rule mining; Genetic algorithm; Threshold setting
Categories
Funding
- Australian large ARC [DP0667060]
- China NSF [60496327, 90718020, 60625204]
- China 973 Program [2008CB317108]
- Chinese Academy of Sciences [06S3011S01]
- Overseas-Returning High-level Talent Research Program of China Hunan-Resource Ministry
- MOE [07JJD720044]
- Guangxi NSF
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We design a genetic algorithm-based strategy for identifying association rules without specifying actual minimum support. In this approach, an elaborate encoding method is developed, and the relative confidence is used as the fitness function. With genetic algorithm, a global search can be performed and system automation is implemented, because our model does not require the user-specified threshold of minimum support. Furthermore, we expand this strategy to cover quantitative association rule discovery. For efficiency, we design a generalized FP-tree to implement this algorithm. We experimentally evaluate our approach, and demonstrate that our algorithms significantly reduce the computation costs and generate interesting association rules only. (c) 2008 Elsevier Ltd. All rights reserved.
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