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Traditional pattern mining algorithms generally suffer from a lack offlexibility. In this paper, we propose a SAT formulation of the problem tosuccessfully mine frequent flexible sequences occurring in transactionaldatasets. Our SAT-based approach can easily be extended with extra constraintsto address a broad range of pattern mining applications. To demonstrate thisclaim, we formulate and add several constraints, such as gap and spanconstraints, to our model in order to extract more specific patterns. We alsouse interactive solving to perform important derived tasks, such as closedpattern mining or maximal pattern mining. Finally, we prove the practicalfeasibility of our SAT model by running experiments on two real datasets.

A SAT model to mine flexible sequences in transactional datasets