  Frequent itemset mining is an essential part of data analysis and data
mining. Recent works propose interesting SAT-based encodings for the problem of
discovering frequent itemsets. Our aim in this work is to define strategies for
adapting SAT solvers to such encodings in order to improve models enumeration.
In this context, we deeply study the effects of restart, branching heuristics
and clauses learning. We then conduct an experimental evaluation on SAT-Based
itemset mining instances to show how SAT solvers can be adapted to obtain an
efficient SAT model enumerator.
