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

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dc.contributor.authorNawapornanan, Chayanan
dc.contributor.authorBoonjing, Veera
dc.date.accessioned2026-08-06T10:03:42Z
dc.date.available2026-08-06T10:03:42Z
dc.date.issued2012-01-01
dc.description.abstractThis 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.
dc.identifier.citationProceedings International Conference on Machine Learning and Cybernetics, 1, 96-101, 2012
dc.identifier.doi10.1109/ICMLC.2012.6358893
dc.identifier.issn2160133X
dc.identifier.other2-s2.0-84871650926
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/4041
dc.sourceProceedings International Conference on Machine Learning and Cybernetics
dc.subjectAssociation rules
dc.subjectData mining
dc.subjectKnowledge discovery
dc.subjectShare-frequent itemsets
dc.titleAn efficient algorithm for mining complete share-frequent itemsets using BitTable and heuristics
dc.typeConference Paper

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