An efficient algorithm to update large itemsets with early pruning
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Maintenance of Discovered Association Rules in Large Databases: An Incremental Updating Technique
ICDE '96 Proceedings of the Twelfth International Conference on Data Engineering
Efficiently Mining Maximal Frequent Itemsets
ICDM '01 Proceedings of the 2001 IEEE International Conference on Data Mining
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
A General Incremental Technique for Maintaining Discovered Association Rules
Proceedings of the Fifth International Conference on Database Systems for Advanced Applications (DASFAA)
Concise Representations of Association Rules
Proceedings of the ESF Exploratory Workshop on Pattern Detection and Discovery
Moment: Maintaining Closed Frequent Itemsets over a Stream Sliding Window
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Efficient itemset generator discovery over a stream sliding window
Proceedings of the 18th ACM conference on Information and knowledge management
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This paper presents an efficient algorithm for maintaining the generator representation in dynamic datasets. The generators representation is a kind of lossless, concise representation of the set of frequent itemsets. Furthermore, the algorithm utilizes a novel optimization based on generators borders for the first time in the literature. Generators borders are the borderline between frequent generators and other itemsets. New frequent generators can be generated through monitoring them. Experiments show that our algorithm is more efficient than previous solutions.