  Finding statistically significant interactions between binary variables is
computationally and statistically challenging in high-dimensional settings, due
to the combinatorial explosion in the number of hypotheses. Terada et al.
recently showed how to elegantly address this multiple testing problem by
excluding non-testable hypotheses. Still, it remains unclear how their approach
scales to large datasets.
  We here proposed strategies to speed up the approach by Terada et al. and
evaluate them thoroughly in 11 real-world benchmark datasets. We observe that
one approach, incremental search with early stopping, is orders of magnitude
faster than the current state-of-the-art approach.
