# Discovering Useful Compact Sets of Sequential Rules in a Long Sequence

arXiv: https://arxiv.org/abs/2109.07519 · HTML: https://mlsanity.com/paper/2109.07519/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 27 references that are in the map.
- First posted on arXiv 15 Sep 2021.

## Builds on (most cited first, 5 of 5)

- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [The Long and the Short of It: Summarising Event Sequences with Serial Episodes](https://mlsanity.com/paper/1902.02834/) (2019)
- [Efficiently Summarising Event Sequences with Rich Interleaving Patterns](https://mlsanity.com/paper/1701.08096/) (2017)
- [Keeping it Short and Simple: Summarising Complex Event Sequences with Multivariate Patterns](https://mlsanity.com/paper/1512.07056/) (2015)
- [Mining Closed Strict Episodes](https://mlsanity.com/paper/1904.09231/) (2019)

## Built on by (most cited first, 2 of 2)

- [MDL-based Compressing Sequential Rules](https://mlsanity.com/paper/2212.10252/) (2022)
- [Seqret: Mining Rule Sets from Event Sequences](https://mlsanity.com/paper/2505.06049/) (2025)

## Abstract (arXiv)

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.