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 data analyst than the usual local
patterns, while reducing the number of patterns. In parallel, recent works
investigating relationships between data mining and constraint programming (CP)
show that the CP paradigm is a nice framework to model and mine such patterns
in a declarative and generic way. We present a constraint-based language which
enables us to define queries addressing patterns sets and global patterns. The
usefulness of such a declarative approach is highlighted by several examples
coming from the clustering based on associations. This language has been
implemented in the CP framework.Comment: 12 page