Anomaly Rule Detection in Sequence Data

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 f...

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Veröffentlicht in:IEEE transactions on knowledge and data engineering 2023-12, Vol.35 (12), p.12095-12108
Hauptverfasser: Gan, Wensheng, Chen, Lili, Wan, Shicheng, Chen, Jiahui, Chen, Chien-Ming
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container_issue 12
container_start_page 12095
container_title IEEE transactions on knowledge and data engineering
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creator Gan, Wensheng
Chen, Lili
Wan, Shicheng
Chen, Jiahui
Chen, Chien-Ming
description 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.
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subjects Algorithms
Anomalies
Anomaly detection
Data analysis
Frequency analysis
Itemsets
Outliers (statistics)
Security
sequence
sequential rule
Task analysis
Upper bound
Upper bounds
utility mining
title Anomaly Rule Detection in Sequence Data
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