# Memory-Efficient Sequential Pattern Mining with Hybrid Tries

arXiv: https://arxiv.org/abs/2202.06834 · HTML: https://mlsanity.com/paper/2202.06834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 5 of its 50 references that are in the map.
- First posted on arXiv 6 Feb 2022.

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

- [Memory-Efficient Backpropagation Through Time](https://mlsanity.com/paper/1606.03401/) (2016)
- [The Music Streaming Sessions Dataset](https://mlsanity.com/paper/1901.09851/) (2018)
- [Memory-Efficient Implementation of DenseNets](https://mlsanity.com/paper/1707.06990/) (2017)
- [A Survey of Parallel Sequential Pattern Mining](https://mlsanity.com/paper/1805.10515/) (2018)
- [Constraint-based Sequential Pattern Mining with Decision Diagrams](https://mlsanity.com/paper/1811.06086/) (2018)

## Abstract (arXiv)

This paper develops a memory-efficient approach for Sequential Pattern Mining (SPM), a fundamental topic in knowledge discovery that faces a well-known memory bottleneck for large data sets. Our methodology involves a novel hybrid trie data structure that exploits recurring patterns to compactly store the data set in memory; and a corresponding mining algorithm designed to effectively extract patterns from this compact representation. Numerical results on small to medium-sized real-life test instances show an average improvement of 85% in memory consumption and 49% in computation time compared to the state of the art. For large data sets, our algorithm stands out as the only capable SPM approach within 256GB of system memory, potentially saving 1.7TB in memory consumption.