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Explainable Distance-Based Outlier Detection in Data Streams
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DOI：10.1109/ACCESS.2022.3172345.png)
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摘要
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Explaining outliers is a topic that attracts a lot of interest; however existing proposals focus on the identification of the relevant dimensions. We extend this rationale for unsupervised distance-based outlier detection, and through investigating subspaces, we propose a novel labeling of outliers in a manner that is intuitive for the user and does not require any training at runtime. Moreover, our solution is applicable to online settings and a complete prototype for detecting and explaining outliers in data streams using massive parallelism has been implemented. Our solution is evaluated in terms of both the quality of the labels derived and the performance.
Keyword:
Anomaly detection
Labeling
Transforms
Training
Real-time systems
Proposals
Licenses
Data streams
distance-based outlier detection
distributed
explainability
flink
期刊
IF:
3.6
论文数:
9.8W
被引数:
29.4W
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