Distance-based Outlier Detection
Distance-Based Outlier Detection is an outlier detection method that identifies outliers based on the principle that outliers are data points whose local neighborhood, defined by a distance metric, is sparser compared to the neighborhood of non-outlier points.
253
Publications
25.4K
Citations
633
Authors
301
Institutions
Publications per year
2017–2026
96
253
Algorithms for Mining Distance-Based Outliers in Large Datasets
Edwin M. Knorr, Raymond T. Ng · 1998 · 1.5K citations
Distance-based outliers: algorithms and applications
Edwin M. Knorr, Raymond T. Ng, Vladimir Tucakov · The VLDB Journal · 2000 · 1.2K citations
Efficient algorithms for mining outliers from large data sets
Sridhar Ramaswamy, Rajeev Rastogi, Kyuseok Shim · 2000 · 1.1K citations · Full text
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Institute for High Performance Computing and Networking, Italy
5 publications · 418 citations
5
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University of Calabria
Rende, Italy
9 h-index
14 publications
1.8K citations
National Research Council
Rome, Italy
6 h-index
9 publications
1.7K citations
Institute for High Performance Computing and Networking
Rende, Italy
6 h-index
9 publications
1.7K citations

Northwestern Polytechnical University
Xi'an, China
5 h-index
11 publications
606 citations
University of Tsukuba
Tsukuba, Japan
5 h-index
8 publications
982 citations
The Ohio State University
Columbus, United States
5 h-index
10 publications
894 citations

University of British Columbia
Vancouver, Canada
4 h-index
8 publications
5.6K citations
Carnegie Mellon University
Pittsburgh, United States
4 h-index
6 publications
1.8K citations
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TKDE · 8 publications · 1.3K citations
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8 publications · 738 citations
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4 publications · 2K citations
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4 publications · 67 citations
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4 publications · 230 citations
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