The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Detecting anomalous records in categorical datasets
Abstract
We consider the problem of detecting anomalies in high aritycategorical datasets. In most applications, anomalies are defined as datapoints that are "abnormal". Quite often we have access to data which consists mostly of normal records, a long with a small percentage of unlabelled anomalous records. We are interested in the problem of unsupervised anomaly detection, where we use the unlabelled data for training, and detect records that do not follow the definition of normality.
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Cites
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
A Geometric Framework for Unsupervised Anomaly Detection: Detecting Intrusions in Unlabeled Data
Eskin, E., Arnold, A., Prerau, M., Portnoy, L., & Stolfo, S. (2002) · Advances in Information Security · doi:10.1007/978-1-4615-0953-0_4 · Cited by 683
Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
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
HoloClean: holistic data repairs with probabilistic inference
Rekatsinas, T., Chu, X., Ilyas, I. F., & Ré, C. (2017) · Proceedings of the VLDB Endowment · doi:10.14778/3137628.3137631 · Cited by 480
Design of inferential sensors in the process industry: A review of Bayesian methods
Khatibisepehr, S., Huang, B., & Khare, S. (2013) · Journal of Process Control · doi:10.1016/j.jprocont.2013.05.007 · Cited by 262
Can language models automate data wrangling?
Jaimovitch-López, G., Ferri, C., Hernández-Orallo, J., Martínez-Plumed, F., & Ramírez-Quintana, M. J. (2023) · Machine Learning · doi:10.1007/s10994-022-06259-9 · Cited by 48
Metadata
- Authors
- Das, K., & Schneider, J.
- Venue
- Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 2007-08-12
- Pages
- 220-229
- OpenAlex
- W2162774438
- Cited by
- 180
- References
- 2
- FWCI
- 4.34
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Das, K., & Schneider, J. (2007). Detecting anomalous records in categorical datasets. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining. https://doi.org/10.1145/1281192.1281219
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