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Progress in Outlier Detection Techniques: A Survey
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Detecting outliers is a significant problem that has been studied in various research and application areas. Researchers continue to design robust schemes to provide solutions to detect outliers efficiently. In this survey, we present a comprehensive and organized review of the progress of outlier detection methods from 2000 to 2019. First, we offer the fundamental concepts of outlier detection and then categorize them into different techniques from diverse outlier detection techniques, such as distance-, clustering-, density-, ensemble-, and learning-based methods. In each category, we introduce some state-of-the-art outlier detection methods and further discuss them in detail in terms of their performance. Second, we delineate their pros, cons, and challenges to provide researchers with a concise overview of each technique and recommend solutions and possible research directions. This paper gives current progress of outlier detection techniques and provides a better understanding of the different outlier detection methods. The open research issues and challenges at the end will provide researchers with a clear path for the future of outlier detection methods.
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(1997)
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Self-citation
Metadata
- Authors
- Wang, H., Bah, M. J., & Hammad, M.
- Venue
- IEEE Access
- Published
- 2019
- Volume
- 7
- Pages
- 107964-108000
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W2966559104
- Cited by
- 523
- References
- 181
- FWCI
- 23.94
- Type
- journal article
- Language
- en
- Open access
- gold
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.
What counts as a claim ›How we measure confidence ›How we check this ›
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.