Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 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
Turbo-charging vertical mining of large databases
SIGMOD '00 Proceedings of the 2000 ACM SIGMOD international conference on Management of data
A tree projection algorithm for generation of frequent item sets
Journal of Parallel and Distributed Computing - Special issue on high-performance data mining
Scalable Algorithms for Association Mining
IEEE Transactions on Knowledge and Data Engineering
Mining Frequent Item Sets with Convertible Constraints
Proceedings of the 17th International Conference on Data Engineering
H-Mine: Hyper-Structure Mining of Frequent Patterns in Large Databases
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
CT-ITL: efficient frequent item set mining using a compressed prefix tree with pattern growth
ADC '03 Proceedings of the 14th Australasian database conference - Volume 17
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An important problem in data mining is the discovery of association rules that identify relationships among sets of items. Finding frequent itemsets is computationally the most expensive step in association rules mining, and so most of the research attention has been focused on it. In this paper, we present a more efficient algorithm for mining frequent itemsets. In designing our algorithm, we have combined the ideas of pattern-growth, tid-intersection and prefix trees, with significant modifications. We present performance comparisons of our algorithm against the fastest Apriori algorithm, and the recently developed H-Mine algorithm. We have tested all the algorithms using several widely used test datasets. The performance results indicate that our algorithm significantly reduces the processing time for mining frequent itemsets in dense data sets that contain relatively long patterns.