Efficient breadth-first mining of frequent pattern with monotone constraints

摘要

The key point of this article is that, in frequent pattern mining, the most appropriate way of exploiting monotone constraints in conjunction with frequency is to use them in order to reduce the input data; this reduction in turn induces a stronger pruning of the search space of the problem. Following this intuition, we introduce ExAMiner, a breadth-first algorithm that exploits the real synergy of antimonotone and monotone constraints: the total benefit is greater than the sum of the two individual benefits. ExAMiner generalizes the basic idea of the preprocessing algorithm ExAnte (Bonchi et al. 2003(b)), embedding such ideas at all levels of an Apriori-like computation. The resulting algorithm is the generalization of the Apriori algorithm when a conjunction of monotone constraints is conjoined to the frequency antimonotone constraint. Experimental results confirm that this is, so far, the most efficient way of attacking the computational problem in analysis.