  Discovering the key structure of a database is one of the main goals of data
mining. In pattern set mining we do so by discovering a small set of patterns
that together describe the data well. The richer the class of patterns we
consider, and the more powerful our description language, the better we will be
able to summarise the data. In this paper we propose \ourmethod, a novel greedy
MDL-based method for summarising sequential data using rich patterns that are
allowed to interleave. Experiments show \ourmethod is orders of magnitude
faster than the state of the art, results in better models, as well as
discovers meaningful semantics in the form patterns that identify multiple
choices of values.
