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Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection
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LOF: identifying density-based local outliers
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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 2×
“…published over the last decade (e.g. Hodge and Austin 2004; Agyemang et al. 2006; Hadi et al. 2009; Chandola et al. 2009, 2012; Su and Tsai 2011; Zimek et al. 2012), covering a broad selection of algorithmic approaches to outlier detection and quite different application scenarios, there has…”
BackgroundSelf-citationA comprehensive survey of numeric and symbolic outlier mining techniques
Agyemang, M., Barker, K., & Alhajj, R. (2006) · Intelligent Data Analysis · doi:10.3233/ida-2006-10604 · Cited by 164
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Metadata
- Authors
- Schubert, E., Zimek, A., & Kriegel, H.-P.
- Venue
- Data Mining and Knowledge Discovery
- Published
- 2012-12-18
- Volume
- 28
- Issue
- 1
- Pages
- 190-237
- ISSN
- 1384-5810 (ISSN-L)
- 1573-756X
- OpenAlex
- W2075949491
- Cited by
- 300
- References
- 5
- FWCI
- 10.77
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
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- Schubert, E., Zimek, A., & Kriegel, H.-P. (2014). Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Mining and Knowledge Discovery. https://doi.org/10.1007/s10618-012-0300-z
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