Discovering Knowledge using a Constraint-based Language

Discovering pattern sets or global patterns is an attractive issue from the pattern mining community in order to provide useful infor- mation. By combining local patterns satisfying a joint meaning, this ap- proach produces patterns of higher level and thus more useful for the data analyst than the usual local patterns, while reducing the number of pat- terns. 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 exam- ples coming from the clustering based on associations. This language has been implemented in the CP framework.

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