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
Dynamic itemset counting and implication rules for market basket data
SIGMOD '97 Proceedings of the 1997 ACM SIGMOD international conference on Management of data
Mining association rules with multiple minimum supports
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Mining frequent patterns without candidate generation
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
Alternative Interest Measures for Mining Associations in Databases
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
An Efficient Algorithm for Mining Association Rules in Large Databases
VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases
CoMine: Efficient Mining of Correlated Patterns
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
Data Mining: Concepts and Techniques
Data Mining: Concepts and Techniques
An FP-tree based approach for mining all strongly correlated item pairs
CIS'05 Proceedings of the 2005 international conference on Computational Intelligence and Security - Volume Part I
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In association rules mining application, some rules can provide a lot of useful knowledge for us, though these rules have the lower Support, called weak-support mode in this paper. However, in existing Support-Confidence framework, the rules with lower Support will be lost. Thus, this paper puts forward a new association rules mining technique, which sets up the lower support threshold value to ensure the weak-support rules to be mined and applies Csupport measure to recognise weak-support mode. Then, a new measure, called as N-confidence, is used to restrict mining size in generation frequent sets, which can strain away the weak-support rules without correlation. Furthermore, this paper puts forward a new interesting measure to distinguish from the association rules interesting degree. In order to enhance mining efficiency, a novel algorithm, namely FT-Miner, is presented to discover association rules in a forest by using two new data structures, including UFP-Tree and FP-Forest. The experimentation shows that the algorithm not only mines useful and weak-support rules, but also has better capability than classical association rules mining algorithms.