# Seqret: Mining Rule Sets from Event Sequences

arXiv: https://arxiv.org/abs/2505.06049 · HTML: https://mlsanity.com/paper/2505.06049/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 52 references that are in the map.
- First posted on arXiv 9 May 2025.

## Builds on (most cited first, 8 of 8)

- [The Minimum Description Length Principle for Pattern Mining: A Survey](https://mlsanity.com/paper/2007.14009/) (2020)
- [Learning Interpretable Temporal Properties from Positive Examples Only](https://mlsanity.com/paper/2209.02650/) (2022)
- [Skopus: Mining top-k sequential patterns under leverage](https://mlsanity.com/paper/1506.08009/) (2015)
- [Efficiently Summarising Event Sequences with Rich Interleaving Patterns](https://mlsanity.com/paper/1701.08096/) (2017)
- [Mining Periodic Patterns with a MDL Criterion](https://mlsanity.com/paper/1807.01706/) (2018)
- [Discovering Useful Compact Sets of Sequential Rules in a Long Sequence](https://mlsanity.com/paper/2109.07519/) (2021)
- [Significance of Episodes Based on Minimal Windows](https://mlsanity.com/paper/1902.02755/) (2019)
- [MDL-based Compressing Sequential Rules](https://mlsanity.com/paper/2212.10252/) (2022)

## Abstract (arXiv)

Summarizing event sequences is a key aspect of data mining. Most existing methods neglect conditional dependencies and focus on discovering sequential patterns only. In this paper, we study the problem of discovering both conditional and unconditional dependencies from event sequence data. We do so by discovering rules of the form X rightarrow Y where X and Y are sequential patterns. Rules like these are simple to understand and provide a clear description of the relation between the antecedent and the consequent. To discover succinct and non-redundant sets of rules we formalize the problem in terms of the Minimum Description Length principle. As the search space is enormous and does not exhibit helpful structure, we propose the Seqret method to discover high-quality rule sets in practice. Through extensive empirical evaluation we show that unlike the state of the art, Seqret ably recovers the ground truth on synthetic datasets and finds useful rules from real datasets.