Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
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
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on 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
Sampling Large Databases for Association Rules
VLDB '96 Proceedings of the 22th International Conference on Very Large Data Bases
Towards Efficient Re-mining of Frequent Patterns upon Threshold Changes
WAIM '02 Proceedings of the Third International Conference on Advances in Web-Age Information Management
On compressing frequent patterns
Data & Knowledge Engineering
Association rules mining in vertically partitioned databases
Data & Knowledge Engineering - Special issue: WIDM 2004
Association rules mining using heavy itemsets
Data & Knowledge Engineering
Mining frequent tree-like patterns in large datasets
Data & Knowledge Engineering
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Mining frequent patterns has been studied popularly in data mining research. For getting the real useful frequent patterns, one must continually adjust a minimum support threshold. Costly and repeated database scans were done due to not maintaining the frequent patterns discovered. In this paper, we first propose a top-down algorithm for mining frequent patterns, and then present a hybrid algorithm which takes top-down and bottom-up strategies for incremental maintenance of frequent patterns. Efficiency is achieved with the following techniques: large database is compressed into a highly condensed and dynamic frequent pattern tree structure, which avoids repeated database scans, the top-down mining approach adopts a depth first method to avoid the recursive construction and materialization of conditional frequent pattern trees, which dramatically reduces the mining cost. The performance study shows that our algorithm is efficient and scalable for mining frequent patterns, and is an order of magnitude faster than FP_growth and Re-mining.