ExAnte: anticipated data reduction in constrained patterns mining

Constraint pushing techniques have been proven to be effective in reducing the search space in the frequent pattern mining task, and thus in improving efficiency. But while pushing anti-monotone constraints in a level-wise computation of frequent itemsets has been recognized to be always profitable, the case is different for monotone constraints. In fact, monotone constraints have been considered harder to push in the computation and less effective in pruning the search space. In this paper, we show that this prejudice is not founded and introduce ExAnte, a pre-processing data reduction algorithm which reduces dramatically both the search space and the input dataset in constrained frequent patterns mining. Experimental results show a reduction of orders of magnitude, thus enabling a much easier mining task. ExAnte can be used as a pre-processor with any constrained patterns mining algorithm.