  We are interested in understanding the underlying generation process for long
sequences of symbolic events. To do so, we propose COSSU, an algorithm to mine
small and meaningful sets of sequential rules. The rules are selected using an
MDL-inspired criterion that favors compactness and relies on a novel rule-based
encoding scheme for sequences. Our evaluation shows that COSSU can successfully
retrieve relevant sets of closed sequential rules from a long sequence. Such
rules constitute an interpretable model that exhibits competitive accuracy for
the tasks of next-element prediction and classification.
