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DualMiner: A Dual-Pruning Algorithm for Itemsets with Constraints
Authors:Cristian Bucil?  Johannes Gehrke  Daniel Kifer  Walker White
Affiliation:(1) Department of Computer Science, Cornell University, USA;(2) Department of Mathematics, University of Dallas, USA
Abstract:Recently, constraint-based mining of itemsets for questions like ldquofind all frequent itemsets whose total price is at least $50rdquo has attracted much attention. Two classes of constraints, monotone and antimonotone, have been very useful in this area. There exist algorithms that efficiently take advantage of either one of these two classes, but no previous algorithms can efficiently handle both types of constraints simultaneously. In this paper, we present DualMiner, the first algorithm that efficiently prunes its search space using both monotone and antimonotone constraints. We complement a theoretical analysis and proof of correctness of DualMiner with an experimental study that shows the efficacy of DualMiner compared to previous work.
Keywords:data mining  association rules  market basket analysis  constraints in data mining  constrained frequent itemsets
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