Mining association rules between sets of items in large databases
SIGMOD '93 Proceedings of the 1993 ACM SIGMOD international conference on Management of data
Depth first generation of long patterns
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
MAFIA: A Maximal Frequent Itemset Algorithm for Transactional Databases
Proceedings of the 17th International Conference on Data Engineering
Efficiently mining Maximal Frequent Sets in dense databases for discovering association rules
Intelligent Data Analysis
An efficient system for detecting outliers from financial time series
BNCOD'06 Proceedings of the 23rd British National Conference on Databases, conference on Flexible and Efficient Information Handling
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We present MaxDomino, an algorithm for mining maximal frequent sets using a novel concept of dominancy factor of a transaction. We also propose a hashing scheme to collapse the database to a form that contains only unique transactions. Unlike traditional bottom up approach with look-aheads, MaxDomino employs a top down strategy with selective bottom up search for mining maximal sets. Using the connect dataset [Benchmark dataset created by University California, Irvine], our experimental results reveal that MaxDomino outperforms GenMax at higher support levels. Furthermore, our scalability tests show that MaxDomino yields an order of magnitude improvement in speed over GenMax. MaxDomino is especially efficient when the maximal frequent sets are longer.