Discovering pattern sets or global patterns is an attractive issue from the pattern mining community in order to provide useful information. By combining local patterns satisfying a joint meaning, this approach produces patterns of higher level and thus more useful for the end-user than the usual local patterns. In parallel, recent works investigating relationships between data mining and constraint programming (CP) show that the CP paradigm is a powerful framework to model and mine patterns in a declarative and generic way. We present a constraint-based language which enables us to define queries in a declarative way addressing patterns sets and global patterns. By specifying what the task is, rather than providing how the solution should be computed, it is easy to process by stepwise refinements to successfully discover global patterns. The usefulness of the approach is highlighted by several examples coming from the clustering based on associations. All primitive constraints of the language are modeled and solved using the SAT framework. We illustrate the efficiency of our approach through several experiments.