Mining for frequent itemsets can generate an overwhelming number of patterns, often exceeding the size of the original transactional database. One way to deal with this issue is to set filters and interestingness measures. Others advocate the use of constraints to apply to the patterns, either on the form of the patterns or on descriptors of the items in the patterns. However, typically the filtering of patterns based on these constraints is done as a post-processing phase. Filtering the patterns post-mining adds a significant overhead, still suffers from the sheer size of the pattern set and loses the opportunity to exploit those constraints.

In this paper we propose an approach that allows the efficient mining of frequent itemsets patterns, while pushing simultaneously both monotone and anti-monotone constraints during and at different strategic stages of the mining process. Our implementation shows a significant improvement when considering the constraints early and a better performance over Dualminer which also considers both types of constraints.