The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study
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Cites
Hubert, L., & Arabie, P. (1985) · Journal of Classification · doi:10.1007/bf01908075 · Cited by 8,219
The use of ranks to avoid the assumption of normality implicit in the analysis of variance
Friedman, M. (1937) · Journal of the American Statistical Association · doi:10.1080/01621459.1937.10503522 · Cited by 7,005
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
Self-citationLOF: identifying density-based local outliers
Breunig, M. M., Kriegel, H.-P., Ng, R. T., & Sander, J. (2000) · Proceedings of the 2000 ACM SIGMOD international conference on Management of data · doi:10.1145/342009.335388 · Cited by 4,149
Self-citationHawkins, D. M. (1980) · doi:10.1007/978-94-015-3994-4 · Cited by 2,982
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
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 · cited 3×
“Since it is often presumed that the dimensionality of a dataset can be a contributing factor in how well a method performs on the dataset (Houle et al. 2010; Zimek et al. 2012), we first address the question whether this is the case for the given datasets and methods.”
BackgroundMethodSelf-citation
Cited by
Deep Learning for Anomaly Detection: A Review
Pang, G., Shen, C., Cao, L., & Hengel, A. V. D. (2022) · ACM Computing Surveys · doi:10.1145/3439950 · Cited by 2,837
Progress in Outlier Detection Techniques: A Survey
Wang, H., Bah, M. J., & Hammad, M. (2019) · IEEE Access · doi:10.1109/access.2019.2932769 · Cited by 523
Li, Z., Zhao, Y., Han, J., Su, Y., Jiao, R., Wen, X., & Pei, D. (2021) · Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining · doi:10.1145/3447548.3467075 · Cited by 464
Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review
Erhan, L., Ndubuaku, M., Di Mauro, M., Song, W., Chen, M., Fortino, G., Bagdasar, O., & Liotta, A. (2021) · Information Fusion · doi:10.1016/j.inffus.2020.10.001 · Cited by 378
Testing machine learning based systems: a systematic mapping
Riccio, V., Jahangirova, G., Stocco, A., Humbatova, N., Weiss, M., & Tonella, P. (2020) · Empirical Software Engineering · doi:10.1007/s10664-020-09881-0 · Cited by 251
Metadata
- Authors
- Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenková, B., Schubert, E., Assent, I., & Houle, M. E.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2016-01-16
- Volume
- 30
- Issue
- 4
- Pages
- 891-927
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2282861635
- Cited by
- 735
- References
- 7
- FWCI
- 46.47
- Type
- journal article
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. https://app.cochat.ai/papers/10.1007/s10618-015-0444-8
- The paper
- Campos, G. O., Zimek, A., Sander, J., Campello, R. J. G. B., Micenková, B., Schubert, E., Assent, I., & Houle, M. E. (2016). On the evaluation of unsupervised outlier detection: measures, datasets, and an empirical study. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-015-0444-8
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.
