학술논문
Anomaly Rule Detection in Sequence Data
Document Type
Periodical
Source
IEEE Transactions on Knowledge and Data Engineering IEEE Trans. Knowl. Data Eng. Knowledge and Data Engineering, IEEE Transactions on. 35(12):12095-12108 Dec, 2023
Subject
Language
ISSN
1041-4347
1558-2191
2326-3865
1558-2191
2326-3865
Abstract
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 analytics, 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.