Identification of Outliers
Integrity signals
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Each is checked against this paper’s own text: does it say what the citing paper says it does?
Each specific anomaly detection model has its own definition of an anomaly, either explicitly or implicitly.
“As this is informal, each specific anomaly detection model has its own definition of an anomaly, either explicitly or implicitly.”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [81]
Cites
Procedures for Detecting Outlying Observations in Samples
Grubbs, F. E. (1969) · Technometrics · doi:10.1080/00401706.1969.10490657 · Cited by 4,105 · see citing works
Sample criteria for testing outlying observations
Grubbs, F. E. (1950) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729885 · Cited by 1,631 · see citing works
Streuli, H. (1980) · Fresenius Zeitschrift für Analytische Chemie · doi:10.1007/bf00470001 · Cited by 17 · see citing works
Flagged first, then by citations
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Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687 · see citing works
LOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · ACM SIGMOD Record · doi:10.1145/335191.335388 · Cited by 5,207 · see citing works
Graph based anomaly detection and description: a survey
Akoglu, L., Tong, H., & Koutra, D. (2015) · Data Mining and Knowledge Discovery · doi:10.1007/s10618-014-0365-y · Cited by 1,504 · see citing works
A Review on Outlier/Anomaly Detection in Time Series Data
Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2022) · ACM Computing Surveys · doi:10.1145/3444690 · Cited by 1,102 · see citing works
A survey on unsupervised outlier detection in high‐dimensional numerical data
Zimek, A., Schubert, E., & Kriegel, H. (2012) · Statistical Analysis and Data Mining The ASA Data Science Journal · doi:10.1002/sam.11161 · Cited by 864 · see citing works
Performance of Some Resistant Rules for Outlier Labeling
Hoaglin, D. C., Iglewicz, B., & Tukey, J. W. (1986) · Journal of the American Statistical Association · doi:10.1080/01621459.1986.10478363 · Cited by 773 · see citing works
The identification of multiple outliers
Davies, L., & Gather, U. (1993) · Journal of the American Statistical Association · doi:10.1080/01621459.1993.10476339 · Cited by 709 · see citing works
Principles and procedures of exploratory data analysis.
Behrens, J. T. (1997) · Psychological Methods · doi:10.1037/1082-989x.2.2.131 · Cited by 465 · see citing works
Spatiotemporal Data Mining: A Computational Perspective
Shekhar, S., Jiang, Z., Ali, R., Eftelioglu, E., Tang, X., Gunturi, V., & Zhou, X. (2015) · ISPRS International Journal of Geo-Information · doi:10.3390/ijgi4042306 · Cited by 206 · see citing works
FP-outlier: Frequent pattern based outlier detection
He, Z., Xu, X., Huang, Z., & Deng, S. (2005) · Computer Science and Information Systems · doi:10.2298/csis0501103h · Cited by 188 · see citing works
A clustering-based method for unsupervised intrusion detections
Jiang, S., Song, X., Wang, H., Han, J.-J., & Li, Q.-H. (2006) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.11.007 · Cited by 164 · 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
An incorrect data detection method for big data cleaning of machinery condition monitoring
Xu, X., Lei, Y., & Li, Z. (2020) · IEEE Transactions on Industrial Electronics · doi:10.1109/tie.2019.2903774 · Cited by 134 · see citing works
A Survey on Explainable Anomaly Detection
Li, Z., Zhu, Y., & Van Leeuwen, M. (2024) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/3609333 · Cited by 139 · see citing works
Improved quality control processing of peptide-centric LC-MS proteomics data
Matzke, M. M., Waters, K. M., Metz, T. O., Jacobs, J. M., Sims, A. C., Baric, R. S., Pounds, J. G., & Webb-Robertson, B.-J. M. (2011) · Bioinformatics · doi:10.1093/bioinformatics/btr479 · Cited by 107 · see citing works
The misuse and abuse of statistics in biomedical research
Thiese, M. S., Arnold, Z. C., & Walker, S. D. (2015) · Biochemia Medica · doi:10.11613/bm.2015.001 · Cited by 87 · see citing works
A survey on outlier explanations
Panjei, E., Gruenwald, L., Leal, E., Nguyen, C., & Silvia, S. (2022) · The VLDB Journal · doi:10.1007/s00778-021-00721-1 · Cited by 67 · see citing works
Åkerblom, S., Perrin, S., Rivano Fischer, M., & McCracken, L. M. (2021) · Journal of Behavioral Medicine · doi:10.1007/s10865-020-00168-9 · Cited by 52 · 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
Anomaly Detection in a Large-Scale Cloud Platform
Islam, M. S., Pourmajidi, W., Zhang, L., Steinbacher, J., Erwin, T., & Miranskyy, A. (2021) · 2021 IEEE/ACM 43rd International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) · doi:10.1109/icse-seip52600.2021.00024 · Cited by 54 · see citing works
Abokifa, A. A., Haddad, K., Lo, C. S., & Biswas, P. (2017) · World Environmental and Water Resources Congress 2017 · doi:10.1061/9780784480625.063 · Cited by 42 · 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
Åkerblom, S., Perrin, S., Rivano Fischer, M., & McCracken, L. M. (2018) · The Clinical Journal of Pain · doi:10.1097/ajp.0000000000000561 · Cited by 33 · see citing works
Impact of rainfall characteristics on urban stormwater quality using data mining framework
Yan, H., Zhu, D. Z., Loewen, M. R., Zhang, W., Liang, S., Ahmed, S., van Duin, B., Mahmood, K., & Zhao, S. (2023) · Science of The Total Environment · doi:10.1016/j.scitotenv.2022.160689 · Cited by 33 · see citing works
Explainable outlier detection: What, for Whom and Why?
Sejr, J. H., & Schneider-Kamp, A. (2021) · Machine Learning with Applications · doi:10.1016/j.mlwa.2021.100172 · Cited by 32 · see citing works
Åkerblom, S., Perrin, S., Fischer, M. R., & McCracken, L. M. (2016) · International Journal of Behavioral Medicine · doi:10.1007/s12529-016-9539-x · Cited by 26 · see citing works
Cheng, X., Wang, Z., Yang, X., Xu, L., & Liu, Y. (2021) · Computers, Environment and Urban Systems · doi:10.1016/j.compenvurbsys.2021.101627 · Cited by 22 · see citing works
Stefania, G. A., Zanotti, C., Bonomi, T., Fumagalli, L., & Rotiroti, M. (2018) · Waste Management · doi:10.1016/j.wasman.2018.01.043 · Cited by 20 · see citing works
Åkerblom, S., Perrin, S., Fischer, M. R., & McCracken, L. M. (2020) · European Journal of Pain · doi:10.1002/ejp.1530 · Cited by 19 · see citing works
Smart Anomaly Detection Using Data-Driven Techniques in IoT Edge: A Survey
Manokaran, J., & Vairavel, G. (2022) · Lecture Notes in Electrical Engineering · doi:10.1007/978-981-16-8862-1_45 · Cited by 13 · see citing works
Hong, A. E., Malinovsky, P. P., & Damodaran, S. K. (2024) · Digital Threats: Research and Practice · doi:10.1145/3604434 · Cited by 11 · see citing works
- Authors
- Hawkins, D. M.
- Affiliations
- University of Minnesota (US)
- Corresponding author
- Hawkins, D. M.
- Venue
- Springer eBooks
- Publisher
- Springer Netherlands
- Published
- 1980
- OpenAlex
- W2049058890
- Cited by
- 2,982
- References
- 3
- Citations by year
- 2026: 76 · 2025: 152 · 2024: 136 · 2023: 151 · 2022: 182 · 2021: 181 · 2020: 175 · 2019: 180 · 2018: 152 · 2017: 144 · 2016: 124 · 2015: 129 · 2014: 143 · 2013: 108 · 2012: 96
- FWCI
- 3.85
- Type
- book
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Springer eBooks (publishedVersion)
- CERN Document Server (European Organization for Nuclear Research) (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Distribution Estimation and Applications
- Keywords
- outlier detection, outliers, robust statistics
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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