# Anomaly Rule Detection in Sequence Data

arXiv: https://arxiv.org/abs/2111.15026 · HTML: https://mlsanity.com/paper/2111.15026/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 63 references that are in the map.
- First posted on arXiv 29 Nov 2021.

## Builds on (most cited first, 8 of 8)

- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Streaming Social Event Detection and Evolution Discovery in Heterogeneous Information Networks](https://mlsanity.com/paper/2104.00853/) (2021)
- [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)
- [TKUS: Mining Top-K High-Utility Sequential Patterns](https://mlsanity.com/paper/2011.13454/) (2020)
- [Privacy Preserving Utility Mining: A Survey](https://mlsanity.com/paper/1811.07389/) (2018)
- [On-shelf Utility Mining of Sequence Data](https://mlsanity.com/paper/2011.13455/) (2020)

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

- [Towards Target Sequential Rules](https://mlsanity.com/paper/2206.04728/) (2022)
- [Totally-ordered Sequential Rules for Utility Maximization](https://mlsanity.com/paper/2209.13501/) (2022)
- [Artificial Intelligence in Landscape Architecture: A Survey](https://mlsanity.com/paper/2408.14700/) (2024)
- [Advances in Large Language Models for Medicine](https://mlsanity.com/paper/2509.18690/) (2025)
- [Discovering Utility-driven Interval Rules](https://mlsanity.com/paper/2309.16102/) (2023)

## Abstract (arXiv)

Analyzing sequence data usually leads to the discovery of interesting patterns and then anomaly detection. In recent years, numerous frameworks and methods have been proposed to discover interesting patterns in sequence data as well as detect anomalous behavior. However, existing algorithms mainly focus on frequency-driven analytic, and they are challenging to be applied in real-world settings. In this work, we present a new anomaly detection framework called DUOS that enables Discovery of Utility-aware Outlier Sequential rules from a set of sequences. In this pattern-based anomaly detection algorithm, we incorporate both the anomalousness and utility of a group, and then introduce the concept of utility-aware outlier sequential rule (UOSR). We show that this is a more meaningful way for detecting anomalies. Besides, we propose some efficient pruning strategies w.r.t. upper bounds for mining UOSR, as well as the outlier detection. An extensive experimental study conducted on several real-world datasets shows that the proposed DUOS algorithm has a better effectiveness and efficiency. Finally, DUOS outperforms the baseline algorithm and has a suitable scalability.