Abstract. Local patterns are at the core of the discovery of a lot of knowledge from data but their use is limited by their huge number. That it is why discovering global patterns and models from local patterns is an active research field. In this paper, we investigate the relationship between local constraint-based mining and constraint programming and we propose an approach to model and mine patterns combining several local patterns, i.e., patterns defined by global constraints. The user specifies a set of global constraints and a constraint solver generates the whole set of solutions. Our approach takes benefit from the recent progress on mining local patterns and all local constraints which can be inferred from the global ones are pushed by a solver on local patterns. This approach enables us to model in a flexible way any set of constraints combining several local patterns. Experiments show the feasibility of our approach. 1