A SAT model to mine flexible sequences in transactional datasets
  Traditional pattern mining algorithms generally suffer from a lack of
flexibility. In this paper, we propose a SAT formulation of the problem to
successfully mine frequent flexible sequences occurring in transactional
datasets. Our SAT-based approach can easily be extended with extra constraints
to address a broad range of pattern mining applications. To demonstrate this
claim, we formulate and add several constraints, such as gap and span
constraints, to our model in order to extract more specific patterns. We also
use interactive solving to perform important derived tasks, such as closed
pattern mining or maximal pattern mining. Finally, we prove the practical
feasibility of our SAT model by running experiments on two real datasets.
