Discovering outlying aspects in large datasets
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Deep Learning for Anomaly Detection: A Review
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“1 Related Work Compared to the abundance of taxonomies of anomaly detection methods, including but not limited to these surveys throughout the years [3, 36, 37, 78, 86, 161], the categorization of anomaly explanation methods involving XAD techniques has received relatively little attention so far [163, 186, 190, 217,…”
BackgroundMethodInfluentialContextual Outlier Interpretation
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- Authors
- Vinh, N. X., Chan, J., Romano, S., Bailey, J., Leckie, C., Ramamohanarao, K., & Pei, J.
- Affiliations
- The University of Melbourne (AU)
- Simon Fraser University (CA)
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2016-02-09
- Volume
- 30
- Issue
- 6
- Pages
- 1520-1555
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2316630624
- Cited by
- 83
- References
- 30
- Citations by year
- 2026: 4 · 2025: 5 · 2024: 7 · 2023: 9 · 2022: 11 · 2021: 15 · 2020: 12 · 2019: 5 · 2018: 10 · 2017: 4 · 2016: 1
- FWCI
- 4.99
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Data Mining and Knowledge Discovery (publishedVersion)
- RMIT Research Repository (RMIT University Library) (submittedVersion)
- Figshare (submittedVersion)
- Minerva Access (University of Melbourne) (submittedVersion)
- Funders
- University of Melbourne (ECR15)
- Australian Research Council (FT110100112, DP140101969)
- 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
- multidimensional data, subspace search, scoring functions, feature subsets, big data
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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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