2021/11/29 by Wensheng Gan, Gan, Wensheng, Li-Li Chen +9 · 3 citations
Computer Science · #Anomaly (physics) #Anomaly Detection Techniques and Applications #Anomaly detection #Artificial Intelligence (cs.AI) #Artificial intelligence #Baseline (sea) #Computer science #Data mining #Databases (cs.DB) #FOS: Computer and information sciences #Focus (optics) #Network Security and Intrusion Detection #Outlier #Pattern recognition (psychology) #Pruning #Scalability #Sequence (biology) #Set (abstract data type) #Time Series Analysis and Forecasting #cs.AI #cs.DB
paper · pdf · doi:10.48550/arxiv.2111.15026
published in arXiv (Cornell University) (Cornell University) · Preprint. 6 figures, 7 tables
arxiv created 2021/11/29 · openalex publication_date 2021/11/29 · arxiv updated 2021/12/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.