Efficiently mining association rules based on maximum single constraints

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Efficiently mining association rules based on maximum single constraints

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The present paper focuses on the discovery of association rules in which the left-handed and right-handed sides contain in two user-supplied maximum single constraints. If the constraints appear on or differ from a lattice of closed itemsets (together with their typically undersized generators and supports) that have been mined and saved once, we quickly extract the corresponding frequent sub one.

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