# The Long and the Short of It: Summarising Event Sequences with Serial Episodes

arXiv: https://arxiv.org/abs/1902.02834 · HTML: https://mlsanity.com/paper/1902.02834/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Feb 2019.

## Built on by (most cited first, 7 of 7)

- [First Steps of an Approach to the ARC Challenge based on Descriptive Grid Models and the Minimum Description Length Principle](https://mlsanity.com/paper/2112.00848/) (2021)
- [Discovering Useful Compact Sets of Sequential Rules in a Long Sequence](https://mlsanity.com/paper/2109.07519/) (2021)
- [Simplify Your Law: Using Information Theory to Deduplicate Legal Documents](https://mlsanity.com/paper/2110.00735/) (2021)
- [SWoTTeD: An Extension of Tensor Decomposition to Temporal Phenotyping](https://mlsanity.com/paper/2310.01201/) (2023)
- [Role-based lateral movement detection with unsupervised learning](https://mlsanity.com/paper/2108.02713/) (2021)
- [Data is Moody: Discovering Data Modification Rules from Process Event Logs](https://mlsanity.com/paper/2312.14571/) (2023)
- [Clustering of timed sequences -- Application to the analysis of care pathways](https://mlsanity.com/paper/2404.15379/) (2024)

## Abstract (arXiv)

An ideal outcome of pattern mining is a small set of informative patterns, containing no redundancy or noise, that identifies the key structure of the data at hand. Standard frequent pattern miners do not achieve this goal, as due to the pattern explosion typically very large numbers of highly redundant patterns are returned. We pursue the ideal for sequential data, by employing a pattern set mining approach-an approach where, instead of ranking patterns individually, we consider results as a whole. Pattern set mining has been successfully applied to transactional data, but has been surprisingly under studied for sequential data. In this paper, we employ the MDL principle to identify the set of sequential patterns that summarises the data best. In particular, we formalise how to encode sequential data using sets of serial episodes, and use the encoded length as a quality score. As search strategy, we propose two approaches: the first algorithm selects a good pattern set from a large candidate set, while the second is a parameter-free any-time algorithm that mines pattern sets directly from the data. Experimentation on synthetic and real data demonstrates we efficiently discover small sets of informative patterns.