Constraint programming (CP) and propositional satisfiabil- ity (SAT) based framework for modeling and solving pattern mining tasks has gained a considerable audience in recent years. However, this nice declarative and generic framework encounters a scaling problem. The huge size of constraints networks/propositional formulas encoding large datasets is identified as the main bottleneck of most existing ap- proaches. In this paper, we propose a parallel SAT based framework for itemset mining problem to push forward the solving efficiency. The pro- posed approach is based on a divide-and-conquer paradigm, where the transaction database is partitioned using item-based guiding paths. Such decomposition allows us to derive smaller and independent Boolean for- mulas that can be solved in parallel. The performance and scalability of the proposed algorithm are evaluated through extensive experiments on several datasets. We demonstrate that our partition-based parallel SAT approach outperforms other CP approaches even in the sequential case, while significantly reducing the performances gap with specialized approaches