  In this paper, we propose a constraint-based modeling approach for the
problem of discovering frequent gradual patterns in a numerical dataset. This
SAT-based declarative approach offers an additional possibility to benefit from
the recent progress in satisfiability testing and to exploit the efficiency of
modern SAT solvers for enumerating all frequent gradual patterns in a numerical
dataset. Our approach can easily be extended with extra constraints, such as
temporal constraints in order to extract more specific patterns in a broad
range of gradual patterns mining applications. We show the practical
feasibility of our SAT model by running experiments on two real world datasets.
