DualMiner: A dual-pruning algorithm for itemsets with constraints

被引:33
作者
Bucila, C [1 ]
Gehrke, J
Kifer, D
White, W
机构
[1] Cornell Univ, Dept Comp Sci, Ithaca, NY 14853 USA
[2] Univ Dallas, Dept Math, Irving, TX 75062 USA
[3] Cornell Univ, Intelligent Informat Syst Inst, Ithaca, NY 14853 USA
关键词
data mining; association rules; market basket analysis; constraints in data mining; constrained frequent itemsets;
D O I
10.1023/A:1024076020895
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Recently, constraint-based mining of itemsets for questions like "find all frequent itemsets whose total price is at least $50" 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.
引用
收藏
页码:241 / 272
页数:32
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