Pruning and summarizing the discovered associations
KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
Real world performance of association rule algorithms
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Fast Algorithms for Mining Association Rules in Large Databases
VLDB '94 Proceedings of the 20th International Conference on Very Large Data Bases
Finding recent frequent itemsets adaptively over online data streams
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Approximate frequency counts over data streams
VLDB '02 Proceedings of the 28th international conference on Very Large Data Bases
A survey on algorithms for mining frequent itemsets over data streams
Knowledge and Information Systems
Maintaining frequent itemsets over high-speed data streams
PAKDD'06 Proceedings of the 10th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining
Which Is Better for Frequent Pattern Mining: Approximate Counting or Sampling?
DaWaK '09 Proceedings of the 11th International Conference on Data Warehousing and Knowledge Discovery
Discovery of frequent patterns in transactional data streams
Transactions on large-scale data- and knowledge-centered systems II
Discovery of frequent patterns in transactional data streams
Transactions on large-scale data- and knowledge-centered systems II
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We investigate the problem of finding frequent patterns in a continuous stream of transactions. It is recognized that the approximate solutions are usually sufficient and many existing literature explicitly trade off accuracy for speed where the quality of the final approximate counts are governed by an error parameter, 茂戮驴. However, the quantification of 茂戮驴is never simple. By setting a small 茂戮驴, we achieve good accuracy but suffer in terms of efficiency. A bigger 茂戮驴improves the efficiency but seriously degrades the mining accuracy. To alleviate this problem, we offer an alternative which allows user to customize a set of error bounds based on his requirement. Our experimental studies show that the proposed algorithm has high precision, requires less memory and consumes less CPU time.