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
An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Pincer-Search: An Efficient Algorithm for Discovering the Maximum Frequent Set
IEEE Transactions on Knowledge and Data Engineering
Efficient Mining of Intertransaction Association Rules
IEEE Transactions on Knowledge and Data Engineering
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
GMA: an approach for association rules mining on medical images
ICIC'12 Proceedings of the 8th international conference on Intelligent Computing Theories and Applications
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Association rule mining is a very important research topic in the field of data mining. Discovering frequent itemsets is the key process in association rule mining. Traditional association rule algorithms adopt an iterative method to discovery, which requires very large calculations and a complicated transaction process. Because of this, a new association rule algorithm called FAR is proposed in this paper. This new algorithm adopts a Boolean vector "relational calculus" method to discovering frequent itemsets. Experimental results show that this algorithm can quickly discover frequent itemsets and effectively mine potential association rules.