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An efficient algorithm for mining complete share-frequent itemsets using BitTable and heuristics

Author(s)
Nawapornanan, Chayanan
Boonjing, Veera
Date Issued
January 1, 2012
Type
Conference Paper
DOI
10.1109/ICMLC.2012.6358893
Abstract
This paper proposes a new efficient algorithm for mining share-frequent itemsets from BitTable knowledge - extracted once from a transaction database. The knowledge contains sufficient information for such a mining task and provides efficient interactive access. The algorithm finds all share-frequent itemsets by level-wise generating complete promising candidates from a BitTable using heuristics and testing for desired solutions. Simulation results reveal that the proposed algorithm perform significantly better than ShFSM and DCG both runtime and a number of generated candidates. © 2012 IEEE.
Citation
Proceedings International Conference on Machine Learning and Cybernetics, 1, 96-101, 2012
Subjects

Association rules

Data mining

Knowledge discovery

Share-frequent itemse...

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