# Efficiently Summarising Event Sequences with Rich Interleaving Patterns

arXiv: https://arxiv.org/abs/1701.08096 · HTML: https://mlsanity.com/paper/1701.08096/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jan 2017.

## Built on by (most cited first, 5 of 5)

- [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)
- [Seqret: Mining Rule Sets from Event Sequences](https://mlsanity.com/paper/2505.06049/) (2025)

## Abstract (arXiv)

Discovering the key structure of a database is one of the main goals of data mining. In pattern set mining we do so by discovering a small set of patterns that together describe the data well. The richer the class of patterns we consider, and the more powerful our description language, the better we will be able to summarise the data. In this paper we propose ourmethod, a novel greedy MDL-based method for summarising sequential data using rich patterns that are allowed to interleave. Experiments show ourmethod is orders of magnitude faster than the state of the art, results in better models, as well as discovers meaningful semantics in the form patterns that identify multiple choices of values.