Eliminating redundant association rules in multi-level datasets
Association rule mining plays an important job in knowledge and information discovery and there are many approaches available. However, there are still shortcomings with the quality of the discovered rules. Often the number of the discovered rules is huge and many of them are redundant, especially in the case of multi-level datasets. Previous work has shown that the mining of non-redundant rules is a promising approach to solving this problem. However, work by Pasquier et. al.  and Xu & Li [17,18] is only focused on single level datasets. In this paper, we propose an extension to this previous work that allows them to 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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|Item Type:||Conference Paper|
|Additional Information:||Access to the author-version is currently restricted pending permission from the publisher. For more information, please refer to the conference’s website (see hypertext link) or contact the author.|
|Keywords:||Multi-level datasets, Association rules, Redundancy|
|Subjects:||Australian and New Zealand Standard Research Classification > INFORMATION AND COMPUTING SCIENCES (080000) > LIBRARY AND INFORMATION STUDIES (080700) > Information Retrieval and Web Search (080704)|
|Divisions:||Past > QUT Faculties & Divisions > Faculty of Science and Technology|
Past > Schools > School of Software Engineering & Data Communications
|Copyright Owner:||Copyright 2009 CSREA Press|
|Deposited On:||04 Mar 2009 08:28|
|Last Modified:||29 Feb 2012 23:47|
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