Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Advances in knowledge discovery and data mining
Advances in knowledge discovery and data mining
Fast discovery of association rules
Advances in 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
KDD-Cup 2000 organizers' report: peeling the onion
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
Visualizing and fuzzy filtering for discovering temporal trajectories of association rules
Journal of Computer and System Sciences
Efficient temporal pattern mining for humanoid robot
Proceedings of the 1st Amrita ACM-W Celebration on Women in Computing in India
Memory-aware frequent k-itemset mining
KDID'05 Proceedings of the 4th international conference on Knowledge Discovery in Inductive Databases
FaRNet: Fast recognition of high-dimensional patterns from big network traffic data
Computer Networks: The International Journal of Computer and Telecommunications Networking
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Recursive elimination is an algorithm for finding frequent item sets, which is strongly inspired by the FP-growth algorithm and very similar to the H-mine algorithm. It does its work without prefix trees or any other complicated data structures, processing the transactions directly. Its main strength is not its speed (although it is not slow, even outperforms Apriori and Eclat on some data sets), but the simplicity of its structure. Basically all the work is done in one simple recursive function, which can be written with relatively few lines of code.