# Discovering Utility-driven Interval Rules

arXiv: https://arxiv.org/abs/2309.16102 · HTML: https://mlsanity.com/paper/2309.16102/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 55 references that are in the map.
- First posted on arXiv 28 Sep 2023.

## Builds on (most cited first, 12 of 12)

- [A Survey of Utility-Oriented Pattern Mining](https://mlsanity.com/paper/1805.10511/) (2018)
- [A Survey of Parallel Sequential Pattern Mining](https://mlsanity.com/paper/1805.10515/) (2018)
- [ProUM: Projection-based Utility Mining on Sequence Data](https://mlsanity.com/paper/1904.07764/) (2019)
- [TUSQ: Targeted High-Utility Sequence Querying](https://mlsanity.com/paper/2103.16615/) (2021)
- [On-shelf Utility Mining of Sequence Data](https://mlsanity.com/paper/2011.13455/) (2020)
- [Explainable Fuzzy Utility Mining on Sequences](https://mlsanity.com/paper/2103.16519/) (2021)
- [US-Rule: Discovering Utility-driven Sequential Rules](https://mlsanity.com/paper/2111.15020/) (2021)
- [Anomaly Rule Detection in Sequence Data](https://mlsanity.com/paper/2111.15026/) (2021)
- [High Utility Interval-Based Sequences](https://mlsanity.com/paper/1912.11165/) (2019)
- [Statistically Significant Pattern Mining with Ordinal Utility](https://mlsanity.com/paper/2008.10747/) (2020)
- [FIBS: A Generic Framework for Classifying Interval-based Temporal Sequences](https://mlsanity.com/paper/1912.09445/) (2019)
- [Totally-ordered Sequential Rules for Utility Maximization](https://mlsanity.com/paper/2209.13501/) (2022)

## Abstract (arXiv)

For artificial intelligence, high-utility sequential rule mining (HUSRM) is a knowledge discovery method that can reveal the associations between events in the sequences. Recently, abundant methods have been proposed to discover high-utility sequence rules. However, the existing methods are all related to point-based sequences. Interval events that persist for some time are common. Traditional interval-event sequence knowledge discovery tasks mainly focus on pattern discovery, but patterns cannot reveal the correlation between interval events well. Moreover, the existing HUSRM algorithms cannot be directly applied to interval-event sequences since the relation in interval-event sequences is much more intricate than those in point-based sequences. In this work, we propose a utility-driven interval rule mining (UIRMiner) algorithm that can extract all utility-driven interval rules (UIRs) from the interval-event sequence database to solve the problem. In UIRMiner, we first introduce a numeric encoding relation representation, which can save much time on relation computation and storage on relation representation. Furthermore, to shrink the search space, we also propose a complement pruning strategy, which incorporates the utility upper bound with the relation. Finally, plentiful experiments implemented on both real-world and synthetic datasets verify that UIRMiner is an effective and efficient algorithm.