Cited by 446Data Quality and ManagementAnomaly Detection Techniques and ApplicationsMachine Learning and Data Classification

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Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities

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Van den Broeck, Jan, Argeseanu Cunningham, Solveig, Eeckels, Roger, Herbst, Kobus·2005·PLoS Medicine

DOI 10.1371/journal.pmed.0020267PMC PMC1198040

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Inlier: Data value falling within

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Figure 1 A Data-Cleaning Framework (Illustration: Giovanni Maki)CC BY · PubMed Central

Figure 2 Areas within the Range of a Continuous Variable Defined by Hard and Soft Cutoffs for Error Screening and Diagnosis, with Recommended Diagnostic Steps for Data Points Falling in Each Area (Illustration: Giovanni Maki)CC BY · PubMed Central

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Authors: Van den Broeck, J., Argeseanu Cunningham, S., Eeckels, R., & Herbst, K.
Affiliations: University of Pennsylvania (US)
ORCID: Herbst, Kobus
Venue: PLoS Medicine
Publisher: Public Library of Science (PLoS)
Published: 2005-09-06
Volume: 2
Issue: 10
Pages: e267
ISSN: 1549-1277 (ISSN-L)
1549-1676
DOI: 10.1371/journal.pmed.0020267
PMC: PMC1198040
OpenAlex: W1971935224
Cited by: 446
References: 18
Citations by year: 2026: 21 · 2025: 39 · 2024: 35 · 2023: 35 · 2022: 32 · 2021: 47 · 2020: 40 · 2019: 47 · 2018: 36 · 2017: 23 · 2016: 25 · 2015: 17 · 2014: 9 · 2013: 13 · 2012: 5
FWCI: 3.71
Type: journal article
Language: en
Open access: gold
doi.org
Licence: cc-by
creativecommons.org/licenses/by/4.0/ (unspecified)
Locations: PLoS Medicine (publishedVersion, cc-by)
PubMed Central (publishedVersion, cc-by)
PubMed (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders: Wellcome Trust (063009)
Topics: Physical Sciences › Computer Science › Information Systems › Data Quality and Management
Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
Keywords: data cleaning, data quality, data anomalies, outlier detection, data editing, data validation
MeSH terms: Ethics, Professional, Humans, Peer Review, Statistics as Topic, Reproducibility of Results, Epidemiologic Studies, Guidelines as Topic

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Data Cleaning: Detecting, Diagnosing, and Editing Data Abnormalities

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