Please use this identifier to cite or link to this item: https://doi.org/10.1007/978-3-642-03573-9_50
Title: Discovering trends and relationships among rules
Authors: Chen, C. 
Hsu, W. 
Lee, M.L. 
Issue Date: 2009
Source: Chen, C.,Hsu, W.,Lee, M.L. (2009). Discovering trends and relationships among rules. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 5690 LNCS : 603-610. ScholarBank@NUS Repository. https://doi.org/10.1007/978-3-642-03573-9_50
Abstract: Data repositories are constantly evolving and techniques are needed to reveal the dynamic behaviors in the data that might be useful to the user. Existing temporal association rules mining algorithms consider time as another dimension and do not describe the behavior of rules over time. In this work, we introduce the notion of trend fragment to facilitate the analysis of relationships among rules. Two algorithms are proposed to find the relationships among rules. Experiment results on both synthetic and real-world datasets indicate that our approach is scalable and effective. © 2009 Springer Berlin Heidelberg.
Source Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
URI: http://scholarbank.nus.edu.sg/handle/10635/40825
ISBN: 3642035728
ISSN: 03029743
DOI: 10.1007/978-3-642-03573-9_50
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