Concise representations for association rules in multi-level datasets |
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Authors: | Yue Xu Gavin Shaw Yuefeng Li |
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Institution: | School of Information Technology, Queensland University of Technology,Brisbane, 4001,Australia |
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Abstract: | Association rule mining plays an important role in knowledge and information discovery. Often for a dataset, a huge number of rules can be extracted, but many of them are redundant, especially in the case of multi-level datasets. Mining non-redundant rules is a promising approach to solve this problem. However, existing work (Pasquier et al. 2005, Xu & Li 2007) is only focused on single level datasets. In this paper, we firstly present a definition for redundancy and a concise representation called Reliable basis for representing non-redundant association rules, then we propose an extension to the previous work that can remove hierarchically redundant rules from multi-level datasets. We also show that the resulting concise representation of non-redundant association rules is lossless since all association rules can be derived from the representation. Experiments show that our extension can effectively generate multilevel non-redundant rules. |
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Keywords: | Association rule mining redundant association rules closed item_sets multi-level datasets |
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