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SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
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SIGMOD '98 Proceedings of the 1998 ACM SIGMOD international conference on Management of data
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VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
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Mining Non-Redundant Association Rules
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Deriving non-redundant approximate association rules from hierarchical datasets
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AI '08 Proceedings of the 21st Australasian Joint Conference on Artificial Intelligence: Advances in Artificial Intelligence
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WI-IAT '08 Proceedings of the 2008 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology - Volume 03
Mining Negative Relevance Feedback for Information Filtering
WI-IAT '09 Proceedings of the 2009 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology - Volume 01
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KSEM '09 Proceedings of the 3rd International Conference on Knowledge Science, Engineering and Management
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Web Intelligence and Agent Systems
A fast pruning redundant rule method using Galois connection
Applied Soft Computing
A pattern mining approach for information filtering systems
Information Retrieval
Using association rules to solve the cold-start problem in recommender systems
PAKDD'10 Proceedings of the 14th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I
Text mining in negative relevance feedback
Web Intelligence and Agent Systems
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Association rule mining has made many achievements in the area of knowledge discovery. However, the quality of the extracted association rules is a big concern. One problem with the quality of the extracted association rules is the huge size of the extracted rule set. As a matter of fact, very often tens of thousands of association rules are extracted among which many are redundant thus useless. Mining non-redundant rules is a promising approach to solve this problem. The Min-max exact basis proposed by Pasquier et al [Pasquier05] has showed exciting results by generating only non-redundant rules. In this paper, we first propose a relaxing definition for redundancy under which the Min-max exact basis still contains redundant rules; then we propose a condensed representation called Reliable exact basis for exact association rules. The rules in the Reliable exact basis are not only non-redundant but also more succinct than the rules in Min-max exact basis. We prove that the redundancy eliminated by the Reliable exact basis does not reduce the belief to the Reliable exact basis. The size of the Reliable exact basis is much smaller than that of the Min-max exact basis. Moreover, we prove that all exact association rules can be deduced from the Reliable exact basis. Therefore the Reliable exact basis is a lossless representation of exact association rules. Experimental results show that the Reliable exact basis significantly reduces the number of non-redundant rules.