  We present a novel algorithm, Westfall-Young light, for detecting patterns,
such as itemsets and subgraphs, which are statistically significantly enriched
in one of two classes. Our method corrects rigorously for multiple hypothesis
testing and correlations between patterns through the Westfall-Young
permutation procedure, which empirically estimates the null distribution of
pattern frequencies in each class via permutations. In our experiments,
Westfall-Young light dramatically outperforms the current state-of-the-art
approach in terms of both runtime and memory efficiency on popular real-world
benchmark datasets for pattern mining. The key to this efficiency is that
unlike all existing methods, our algorithm neither needs to solve the
underlying frequent itemset mining problem anew for each permutation nor needs
to store the occurrence list of all frequent patterns. Westfall-Young light
opens the door to significant pattern mining on large datasets that previously
led to prohibitive runtime or memory costs.
