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STBAR: a more efficient algorithm for association rule mining

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4 Author(s)
De-chang Pi ; Coll. of Inf. Sci. & Technol., Nanjing Univ. of Aeronaut. & Astronaut., China ; Xiao-lin Qin ; Wang-feng Gu ; Ran Cheng

The discovery of association rules is an important data-mining task for which many algorithms have been proposed. However, the efficiency of these algorithms needs to be improved to handle large datasets. In this paper, we present an algorithm named STBAR for mining association rules, which is improved from TBAR. STBAR employs dynamic pruning, which can effectively cut down the infrequent concatenations. Experiments with UCI dataset show that STBAR is more efficient in speed about 10%-30% than TBAR, which outperforms Apriori, a famous and widely used algorithm.

Published in:

Machine Learning and Cybernetics, 2005. Proceedings of 2005 International Conference on  (Volume:3 )

Date of Conference:

18-21 Aug. 2005

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