Symmetry-Based Pruning in Itemset Mining

In this paper, we show how symmetries, a fundamental structural property, can be used to prune the search space in itemset mining problems. Our approach is based on a dynamic integration of symmetries in APRIORI-like algorithms to prune the set of possible candidate patterns. More precisely, for a given itemset, symmetry can be applied to deduce other itemsets while preserving their properties. We also show that our symmetry-based pruning approach can be extended to the general Mannila and Toivonen pattern mining framework. Experimental results highlight the usefulness and the efficiency of our symmetry-based pruning approach.

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