Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Mining Minimal Non-redundant Association Rules Using Frequent Closed Itemsets
CL '00 Proceedings of the First International Conference on Computational Logic
Data Mining and Knowledge Discovery
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
Fast and Memory Efficient Mining of Frequent Closed Itemsets
IEEE Transactions on Knowledge and Data Engineering
Mining Top-k Fault Tolerant Association Rules by Redundant Pattern Disambiguation in Data Streams
ICICCI '10 Proceedings of the 2010 International Conference on Intelligent Computing and Cognitive Informatics
IGB: a new informative generic base of association rules
PAKDD'05 Proceedings of the 9th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Mining top-k association rules
Canadian AI'12 Proceedings of the 25th Canadian conference on Advances in Artificial Intelligence
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Association rule mining is a fundamental data mining task. However, depending on the choice of the thresholds, current algorithms can become very slow and generate an extremely large amount of results or generate too few results, omitting valuable information.Furthermore, it is well-known that a large proportion of association rules generated are redundant. In previous works, these two problems have been addressed separately. In this paper, we address both of them at the same time by proposing an approximate algorithm named TNR for mining top-k non redundant association rules.