Finding Robust Itemsets under Subsampling

Mining frequent patterns is plagued by the problem of pattern explosion, making pattern reduction techniques a key challenge in pattern mining. In this article we propose a novel theoretical framework for pattern reduction by measuring the robustness of a property of an itemset such as closedness or nonderivability. The robustness of a property is the probability that this property holds on random subsets of the original data. We study four properties, namely an itemset being closed, free, non-derivable, or totally shattered, and demonstrate how to compute the robustness analytically without actually sampling the data. Our concept of robustness has many advantages: Unlike statistical approaches for reducing patterns, we do not assume a null hypothesis or any noise model and, in contrast to noise-tolerant or approximate patterns, the robust patterns for a given property are always a subset of the patterns with this property. If the underlying property is monotonic then the measure is also monotonic, allowing us to efficiently mine robust itemsets. We further derive a parameter-free technique for ranking itemsets that can be used for top-k approaches. Our experiments demonstrate that we can successfully use the robustness measure to reduce the number of patterns and that ranking yields interesting itemsets.