The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Detecting distance-based outliers in streams of data
Abstract
In this work a method for detecting distance-based outliers in data streams is presented. We deal with the sliding window model, where outlier queries are performed in order to detect anomalies in the current window. Two algorithms are presented. The first one exactly answers outlier queries, but has larger space requirements. The second algorithm is directly derived from the exact one, has limited memory requirements and returns an approximate answer based on accurate estimations with a statistical guarantee. Several experiments have been accomplished, confirming the effectiveness of the proposed approach and the high quality of approximate solutions.
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Cites
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
LOF: 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
Distance-based outliers: algorithms and applications
Knorr, E. M., Ng, R. T., & Tucakov, V. (2000) · The VLDB Journal The International Journal on Very Large Data Bases · doi:10.1007/s007780050006 · Cited by 1,195
Cited by
A Review on Outlier/Anomaly Detection in Time Series Data
Blázquez-García, A., Conde, A., Mori, U., & Lozano, J. A. (2022) · ACM Computing Surveys · doi:10.1145/3444690 · Cited by 1,102
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
Outlier Detection: Methods, Models, and Classification
Boukerche, A., Zheng, L., & Alfandi, O. (2021) · ACM Computing Surveys · doi:10.1145/3381028 · Cited by 334
Metadata
- Authors
- Angiulli, F., & Fassetti, F.
- Venue
- Proceedings of the sixteenth ACM conference on Conference on information and knowledge management
- Publisher
- ACM
- Published
- 2007-11-06
- Pages
- 811-820
- OpenAlex
- W2116300222
- Cited by
- 243
- References
- 3
- FWCI
- 5.42
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Angiulli, F., & Fassetti, F. (2007). Detecting distance-based outliers in streams of data. Proceedings of the sixteenth ACM conference on Conference on information and knowledge management. https://doi.org/10.1145/1321440.1321552
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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.
