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We discuss pattern languages for closed pattern mining and learning ofinterval data and distributional data. We first introduce pattern languagesrelying on pairs of intersection-based constraints or pairs of inclusion basedconstraints, or both, applied to intervals. We discuss the encoding of suchinterval patterns as itemsets thus allowing to use closed itemsets mining andformal concept analysis programs. We experiment these languages on clusteringand supervised learning tasks. Then we show how to extend the approach toaddress distributional data.

Closed pattern mining of interval data and distributional data