The DOI matches its Crossref record; 181 of the 181 references it cites were found in a registry or an index.
Progress in Outlier Detection Techniques: A Survey
Open accessAbstract
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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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
UCI Machine Learning Repository
Asuncion, A. (2007) · Medical Entomology and Zoology · Cited by 24,156
(1967) · Cited by 23,719
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Finding Groups in Data: An Introduction to Cluster Analysis
Kaufman, L., & Rousseeuw, P. J. (1990) · Wiley Series in Probability and Statistics · doi:10.1002/9780470316801 · Cited by 7,344 · cited 2×
“Some examples of algorithms that fall under this group include PAM [147], CLARANS [148], K-Means [149], CLARA [147], etc.”
MethodLiu, F. T., Ting, K. M., & Zhou, Z.-H. (2008) · 2008 Eighth IEEE International Conference on Data Mining · doi:10.1109/icdm.2008.17 · Cited by 6,601
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
A Survey of Outlier Detection Methodologies
Hodge, V., & Austin, J. (2004) · Artificial Intelligence Review · doi:10.1007/s10462-004-4304-y · Cited by 3,596
Fake News Detection on Social Media: A Data Mining Perspective
Shu, K., Sliva, A., Wang, S., Tang, J., & Liu, H. (2017) · ACM SIGKDD Explorations Newsletter · doi:10.1145/3137597.3137600 · Cited by 3,304
Automatic subspace clustering of high dimensional data for data mining applications
Agrawal, R., Gehrke, J., Gunopulos, D., & Raghavan, P. (1998) · Proceedings of the 1998 ACM SIGMOD international conference on Management of data · doi:10.1145/276304.276314 · Cited by 2,412
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Boukerche, A., Zheng, L., & Alfandi, O. (2021) · ACM Computing Surveys · doi:10.1145/3381028 · Cited by 334
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Alatrany, A. S., Khan, W., Hussain, A., Kolivand, H., & Al-Jumeily, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-51985-w · Cited by 172
Bloch, L., Friedrich, C. M., & Initiative, F. T. A. D. N. (2021) · Alzheimer's Research & Therapy · doi:10.1186/s13195-021-00879-4 · Cited by 68
Self-Supervised Random Forest on Transformed Distribution for Anomaly Detection
Liu, J., Wang, H., Hang, H., Ma, S., Shen, X., & Shi, Y. (2025) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2023.3348833 · Cited by 28
Self-citationWang, X., Qin, X., Zhu, T., Wang, C., Zhang, S., & Li, M. (2021) · 2021 Edition of the Automatic Speaker Verification and Spoofing Countermeasures Challenge · doi:10.21437/asvspoof.2021-3 · Cited by 27
Anomaly Detection Based on Selection and Weighting in Latent Space
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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
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