  Significant pattern mining, the problem of finding itemsets that are
significantly enriched in one class of objects, is statistically challenging,
as the large space of candidate patterns leads to an enormous multiple testing
problem. Recently, the concept of testability was proposed as one approach to
correct for multiple testing in pattern mining while retaining statistical
power. Still, these strategies based on testability do not allow one to
condition the test of significance on the observed covariates, which severely
limits its utility in biomedical applications. Here we propose a strategy and
an efficient algorithm to perform significant pattern mining in the presence of
categorical covariates with K states.
