Distance-based outliers: algorithms and applications
Integrity signals
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The method in [106] is applicable to any anomaly detection method.
“Applicable to any AD”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [106]
The method in [106] has poor explanation fidelity.
“Poor explanation fidelity”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [106]
Cited by
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A survey on unsupervised outlier detection in high‐dimensional numerical data
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Structural analysis of B-cell epitopes in antibody: protein complexes
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Generative Probabilistic Novelty Detection with Adversarial Autoencoders
Pidhorskyi, S., Almohsen, R., Adjeroh, D. A., & Doretto, G. (2018) · arXiv · doi:10.48550/arxiv.1807.02588 · Cited by 255 · see citing works
A comprehensive survey of numeric and symbolic outlier mining techniques
Agyemang, M., Barker, K., & Alhajj, R. (2006) · Intelligent Data Analysis · doi:10.3233/ida-2006-10604 · Cited by 164 · see citing works
Hierarchical and Networked Vehicle Surveillance in ITS: A Survey
Tian, B., Morris, B. T., Tang, M., Liu, Y., Yao, Y., Gou, C., Shen, D., & Tang, S. (2017) · IEEE Transactions on Intelligent Transportation Systems · doi:10.1109/tits.2016.2552778 · Cited by 160 · see citing works
Chen, P., Liu, H., Xin, R., Carval, T., Zhao, J., Xia, Y., & Zhao, Z. (2022) · The Computer Journal · doi:10.1093/comjnl/bxac085 · Cited by 128 · see citing works
Detecting outlying properties of exceptional objects
Angiulli, F., Fassetti, F., & Palopoli, L. (2009) · ACM Transactions on Database Systems · doi:10.1145/1508857.1508864 · Cited by 88 · see citing works
A Survey of Privacy-Preserving Methods Across Vertically Partitioned Data
Vaidya, J. (2008) · Advances in Database Systems · doi:10.1007/978-0-387-70992-5_14 · Cited by 46 · see citing works
An adaptive weighted one-class SVM for robust outlier detection
Yang, J., Deng, T., & Sui, R. (2016) · Lecture Notes in Electrical Engineering · doi:10.1007/978-3-662-48386-2_49 · Cited by 38 · see citing works
Detection of anomalous vehicle trajectories using federated learning
Koetsier, C., Fiosina, J., Gremmel, J. N., Müller, J. P., Woisetschläger, D. M., & Sester, M. (2022) · ISPRS Open Journal of Photogrammetry and Remote Sensing · doi:10.1016/j.ophoto.2022.100013 · Cited by 31 · see citing works
A Framework for Outlier Description Using Constraint Programming
Kuo, C.-T., & Davidson, I. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.10174 · Cited by 18 · see citing works
Early-warning of ARDS using novelty detection and data fusion
Taoum, A., Mourad-chehade, F., & Amoud, H. (2018) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2018.09.030 · Cited by 14 · see citing works
Borah, A., & Paranjothi, A. (2024) · Electronics · doi:10.3390/electronics14010058 · Cited by 11 · see citing works
- Authors
- Knorr, E. M., Ng, R. T., & Tucakov, V.
- Affiliations
- University of British Columbia (CA)
- ORCID
- Ng, Raymond T.
- Venue
- The VLDB Journal The International Journal on Very Large Data Bases
- Published
- 2000-02-01
- Volume
- 8
- Issue
- 3-4
- Pages
- 237-253
- ISSN
- 0949-877X (ISSN-L)
- 1066-8888
- OpenAlex
- W2061240327
- Cited by
- 1,195
- References
- 0
- Citations by year
- 2026: 16 · 2025: 35 · 2024: 45 · 2023: 44 · 2022: 62 · 2021: 68 · 2020: 68 · 2019: 77 · 2018: 81 · 2017: 59 · 2016: 64 · 2015: 61 · 2014: 59 · 2013: 57 · 2012: 59
- FWCI
- 7.88
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- The VLDB Journal (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Physical Sciences › Computer Science › Information Systems › Data Mining Algorithms and Applications
- Keywords
- distance-based outliers, outlier detection, multidimensional data, high-dimensional data, linear-time algorithms, spatio-temporal data, video surveillance, credit card fraud detection, knowledge discovery
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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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