Cited by 83Anomaly Detection Techniques and ApplicationsData Management and AlgorithmsData Mining Algorithms and Applications

Discovering outlying aspects in large datasets

Vinh, Nguyen Xuan, Chan, Jeffrey, Romano, Simone, Bailey, James, Leckie, Christopher, Ramamohanarao, Kotagiri, Pei, Jian·2016·Data Mining and Knowledge Discovery

Integrity signals

Registry

Crossref · 10.1007/s10618-016-0453-2

Cites

30 of 30 references verified0 citation errors · 0 not checkable

Statistics

Recomputing p-values needs the paper’s full text.

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We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

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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

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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