Computer ScienceMachine Intelligence Research
Anomaly Detection Based on Isolation Mechanisms: A Survey
Y. Cao, H. Xiang, et al.
Isolation-based anomaly detection offers a scalable, low-memory, low-complexity approach to find rare, different instances via random partitioning. This survey reviews state-of-the-art methods, partitioning strategies, scoring functions, extensions for streaming, time series, trajectory and image data, and outlines open challenges. This research was conducted by the authors listed in the <Authors> tag.
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