The DOI matches its Crossref record; 29 of the 29 references it cites were found in a registry or an index.
A comparative study of RNN for outlier detection in data mining
Abstract
We have proposed replicator neural networks (RNNs) for outlier detection. We compare RNN for outlier detection with three other methods using both publicly available statistical datasets (generally small) and data mining datasets (generally much larger and generally real data). The smaller datasets provide insights into the relative strengths and weaknesses of RNNs. The larger datasets in particular test scalability and practicality of application.
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Cites
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Cited by
Chandola, V., Banerjee, A., & Kumar, V. (2009) · ACM Computing Surveys · doi:10.1145/1541880.1541882 · Cited by 11,687
Isolation-Based Anomaly Detection
Liu, F. T., Ting, K. M., & Zhou, Z.-H. (2012) · ACM Transactions on Knowledge Discovery from Data · doi:10.1145/2133360.2133363 · Cited by 2,063
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Anomaly Detection Using Autoencoders with Nonlinear Dimensionality Reduction
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Deep Learning for Anomaly Detection: A Survey
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Metadata
- Authors
- Williams, G., Baxter, R., He, H., Hawkins, S., & Gu, L.
- Venue
- 2002 IEEE International Conference on Data Mining, 2002. Proceedings.
- Publisher
- IEEE Comput. Soc
- Published
- 2003-06-26
- Pages
- 709-712
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2111160323
- Cited by
- 277
- References
- 29
- FWCI
- 2.95
- Type
- conference paper
- Language
- en
- Open access
- closed
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