An effective hash-based algorithm for mining association rules
SIGMOD '95 Proceedings of the 1995 ACM SIGMOD international conference on Management of data
Is Sampling Useful in Data Mining? A Case in the Maintenance of Discovered Association Rules
Data Mining and Knowledge Discovery
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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Mining Incremental Association Rules with Generalized FP-Tree
AI '02 Proceedings of the 15th Conference of the Canadian Society for Computational Studies of Intelligence on Advances in Artificial Intelligence
Using Data Mining Algorithms for Statistical Learning of a Software Agent
KES-AMSTA '07 Proceedings of the 1st KES International Symposium on Agent and Multi-Agent Systems: Technologies and Applications
ICDM'10 Proceedings of the 10th industrial conference on Advances in data mining: applications and theoretical aspects
Rule mining for dynamic databases
IWDC'04 Proceedings of the 6th international conference on Distributed Computing
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As new transactions update data sources and subsequently the data warehouse, the previously discovered association rules in the old database may no longer be interesting rules in the new database. Furthermore, some new interesting rules may appear in the new database. This paper presents a new algorithm for efficiently maintaining discovered association rules in the updated database, which starts by computing the high n level large itemsets in the new database using the available high n level large itemsets in the old database. Some parts of the n - 1, n - 2, . . ., 1 level large itemsets can then be quickly generated by applying the apriori property, thereby avoiding the overhead of calculating many lower level large itemsets that involve huge table scans.