The DOI matches its Crossref record.
Outlier Analysis
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
In the citing paper
“In the past two decades, most research on anomaly detection has focused on improving the accuracy of the detection, while largely ignoring the explainability of the corresponding methods and thus leaving the explanation of outcomes to practitioners.”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [1]
In the citing paper
“Since the seminal work in [105], anomaly detection has been well studied and there exists a plethora of comprehensive surveys and reviews on it, including but not limited to References [1,5,25,36,37,134,135,161,165,231].”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [1]
Citations
Cites
Still being checked.
Cited by
Sorted by citations
PointASNL: Robust Point Clouds Processing Using Nonlocal Neural Networks With Adaptive Sampling
Yan, X., Zheng, C., Li, Z., Wang, S., & Cui, S. R. (2020) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr42600.2020.00563 · Cited by 709
Deep Learning for Anomaly Detection in Time-Series Data: Review, Analysis, and Guidelines
Choi, K., Yi, J., Park, C., & Yoon, S. (2021) · IEEE Access · doi:10.1109/access.2021.3107975 · Cited by 660
ECOD: Unsupervised Outlier Detection Using Empirical Cumulative Distribution Functions
Li, Z., Zhao, Y., Hu, X., Botta, N., Ionescu, C., & Chen, G. H. (2022) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2022.3159580 · Cited by 516
A comprehensive survey of anomaly detection techniques for high dimensional big data
Thudumu, S., Branch, P., Jin, J., & Singh, J. (2020) · Journal Of Big Data · doi:10.1186/s40537-020-00320-x · Cited by 488
PointCleanNet : Learning to Denoise and Remove Outliers from Dense Point Clouds
Rakotosaona, M., Barbera, V. L., Guerrero, P., Mitra, N. J., & Ovsjanikov, M. (2019) · Computer Graphics Forum · doi:10.1111/cgf.13753 · Cited by 381
Metadata
- Authors
- Aggarwal, C. C.
- Venue
- Data Mining
- Publisher
- Springer International Publishing
- Published
- 2015
- Pages
- 237-263
- OpenAlex
- W4239954780
- Cited by
- 305
- References
- 0
- Citations by year
- 2026: 4 · 2025: 12 · 2024: 18 · 2023: 22 · 2022: 33 · 2021: 63 · 2020: 60 · 2019: 34 · 2018: 29 · 2017: 18 · 2016: 12
- FWCI
- 39.18
- Type
- book chapter
- Language
- en
- Open access
- closed
- Licence
- Locations
- Springer eBooks (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
- Keywords
- outlier analysis, outliers, anomaly detection
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
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