  We discuss pattern languages for closed pattern mining and learning of
interval data and distributional data. We first introduce pattern languages
relying on pairs of intersection-based constraints or pairs of inclusion based
constraints, or both, applied to intervals. We discuss the encoding of such
interval patterns as itemsets thus allowing to use closed itemsets mining and
formal concept analysis programs. We experiment these languages on clustering
and supervised learning tasks. Then we show how to extend the approach to
address distributional data.
