The DOI matches its Crossref record, with a small difference in title or year; 76 of the 76 references it cites were found in a registry or an index.
A comprehensive survey of numeric and symbolic outlier mining techniques
Abstract
Data that appear to have different characteristics than the rest of the population are called outliers. Identifying outliers from huge data repositories is a very complex task called outlier mining. Outlier mining has been akin to finding needles in
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“Since the seminal work in [105], anomaly detection has been well studied and there exists a plethora of comprehensive surveys and reviews on it, including but not limited to References [1,5,25,36,37,134,135,161,165,231].”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [5]
In the citing paper
“According to Agyemang et al. [5], distribution-based anomaly detection techniques are intrinsically explainable.”
From A Survey on Explainable Anomaly Detection (2024) · cites this paper as [5]
Citations
Cites
Data mining: concepts and techniques
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Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · Proceedings of the 1993 ACM SIGMOD international conference on Management of data · doi:10.1145/170035.170072 · Cited by 14,907
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Robust Regression and Outlier Detection
Piepel, G. F., Rousseeuw, P. J., & Leroy, A. M. (1989) · Technometrics · doi:10.2307/1268828 · Cited by 6,183
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
Mining association rules between sets of items in large databases
Agrawal, R., Imieliński, T., & Swami, A. (1993) · ACM SIGMOD Record · doi:10.1145/170036.170072 · Cited by 4,551
BIRCH: an efficient data clustering method for very large databases
Zhang, T., Ramakrishnan, R., & Livny, M. (1996) · Proceedings of the 1996 ACM SIGMOD international conference on Management of data - SIGMOD '96 · doi:10.1145/233269.233324 · Cited by 3,935
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
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Cited by
Sorted by citations
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Pimentel, M. A. F., Clifton, D. A., Clifton, L., & Tarassenko, L. (2014) · Signal Processing · doi:10.1016/j.sigpro.2013.12.026 · Cited by 1,649
Ngai, E. W. T., Hu, Y., Wong, Y. H., Chen, Y., & Sun, X. (2011) · Decision Support Systems · doi:10.1016/j.dss.2010.08.006 · Cited by 1,361
Network Anomaly Detection: Methods, Systems and Tools
Bhuyan, M. H., Bhattacharyya, D. K., & Kalita, J. K. (2013) · IEEE Communications Surveys & Tutorials · doi:10.1109/surv.2013.052213.00046 · Cited by 1,358
Hierarchical Density Estimates for Data Clustering, Visualization, and Outlier Detection
Campello, R. J. G. B., Moulavi, D., Zimek, A., & Sander, J. (2015) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2733381 · Cited by 915
Metadata
- Authors
- Agyemang, M., Barker, K., & Alhajj, R.
- Affiliations
- University of Calgary (CA)
- ORCID
- Barker, Ken
- Venue
- Intelligent Data Analysis
- Publisher
- SAGE Publications
- Published
- 2006-11-16
- Volume
- 10
- Issue
- 6
- Pages
- 521-538
- ISSN
- 1088-467X (ISSN-L)
- 1571-4128
- OpenAlex
- W2097627964
- Cited by
- 164
- References
- 76
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 4 · 2023: 6 · 2022: 21 · 2021: 7 · 2020: 9 · 2019: 17 · 2018: 15 · 2017: 15 · 2016: 8 · 2015: 11 · 2014: 7 · 2013: 13 · 2012: 13
- FWCI
- 2.28
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Intelligent Data Analysis (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Anomaly Detection Techniques and Applications
- Physical Sciences › Mathematics › Statistics and Probability › Advanced Statistical Methods and Models
- Physical Sciences › Computer Science › Artificial Intelligence › Advanced Clustering Algorithms Research
- Keywords
- outlier detection, numerical data, symbolic data, data mining techniques
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