Discovering Useful Compact Sets of Sequential Rules in a Long Sequence
This work addresses the need for interpretable models in sequence analysis, though it appears incremental as it builds on existing rule-mining and MDL approaches.
The paper tackles the problem of understanding the generation process of long symbolic sequences by proposing COSSU, an algorithm that mines compact sets of sequential rules using an MDL-inspired criterion, resulting in interpretable models with competitive accuracy for next-element prediction and classification tasks.
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.