Constrained clustering - finding clusters that satisfy userspecified constraints - aims at providing more relevant clusters by adding constraints enforcing required properties. Leveraging the recent progress in declarative and constraint-based pattern mining, we propose an effective constraint-clustering approach handling a large set of constraints which are described by a generic constraint-based language. Starting from an initial solution, queries can easily be refined in order to focus on more interesting clustering solutions. We show how each constraint (and query) is encoded in SAT and solved by taking benefit from several features of SAT solvers. Experiments performed using MiniSat on several datasets from the UCI repository show the feasibility and the advantages of our approach.