# Constraint-based Sequential Pattern Mining with Decision Diagrams

arXiv: https://arxiv.org/abs/1811.06086 · HTML: https://mlsanity.com/paper/1811.06086/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 14 Nov 2018.

## Built on by (most cited first, 3 of 3)

- [VideoPro: A Visual Analytics Approach for Interactive Video Programming](https://mlsanity.com/paper/2308.00401/) (2023)
- [Dichotomic Pattern Mining with Applications to Intent Prediction from Semi-Structured Clickstream Datasets](https://mlsanity.com/paper/2201.09178/) (2022)
- [Memory-Efficient Sequential Pattern Mining with Hybrid Tries](https://mlsanity.com/paper/2202.06834/) (2022)

## Abstract (arXiv)

Constrained sequential pattern mining aims at identifying frequent patterns on a sequential database of items while observing constraints defined over the item attributes. We introduce novel techniques for constraint-based sequential pattern mining that rely on a multi-valued decision diagram representation of the database. Specifically, our representation can accommodate multiple item attributes and various constraint types, including a number of non-monotone constraints. To evaluate the applicability of our approach, we develop an MDD-based prefix-projection algorithm and compare its performance against a typical generate-and-check variant, as well as a state-of-the-art constraint-based sequential pattern mining algorithm. Results show that our approach is competitive with or superior to these other methods in terms of scalability and efficiency.