Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Finding interesting rules from large sets of discovered association rules
CIKM '94 Proceedings of the third international conference on Information and knowledge management
Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Generating non-redundant association rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Multi-level organization and summarization of the discovered rules
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Parallel and Distributed Association Mining: A Survey
IEEE Concurrency
A New Approach to Online Generation of Association Rules
IEEE Transactions on Knowledge and Data Engineering
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Mining Non-Redundant Association Rules
Data Mining and Knowledge Discovery
Adaptive estimated maximum-entropy distribution model
Information Sciences: an International Journal
Mining Multiple Level Non-redundant Association Rules through Two-Fold Pruning of Redundancies
MLDM '09 Proceedings of the 6th International Conference on Machine Learning and Data Mining in Pattern Recognition
FAR-miner: a fast and efficient algorithm for fuzzy association rule mining
International Journal of Business Intelligence and Data Mining
A prediction framework based on contextual data to support Mobile Personalized Marketing
Decision Support Systems
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Association rule mining has a capability to find hidden correlations among different items within a dataset. To find hidden correlations, it uses two important thresholds known as support and confidence. However, association rule mining algorithms produce many redundant rules though it uses above thresholds. Indeed such redundant rules seem as a main impediment to efficient utilization discovered association rules, and should be removed. To achieve this aim, in the paper, we present several redundant rule elimination methods that first identify the rules that have similar meaning and then eliminate those rules. Furthermore, our methods eliminate redundant rules in such a way that they never drop any higher confidence or interesting rules from the resultant ruleset. The experimental evaluation shows that our proposed methods eliminate a significant number of redundant rules.