Closed pattern mining of interval data and distributional data
This work addresses pattern mining for specialized data types like intervals and distributions, which is incremental as it builds on existing itemset mining methods.
The paper tackles the problem of mining closed patterns from interval and distributional data by introducing pattern languages based on intersection and inclusion constraints, which are encoded as itemsets for use with existing mining tools. It experiments with these languages on clustering and supervised learning tasks, but does not report specific numerical results.
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.