Distance-based Anomaly Detection
Distance-Based Anomaly Detection is an anomaly detection method that identifies outliers by measuring the proximity of data points to their nearest neighbors, distinguishing itself through the use of distance metrics to define anomalous behavior.
169
Publications
16.9K
Citations
515
Authors
226
Institutions
Publications per year
2017–2026
75
Variants
Distance-based Outlier Detection
Parents
169
Fei Tony Liu, Kai Ming Ting, Zhi‐Hua Zhou · 2008 · 5.6K citations
Algorithms for Mining Distance-Based Outliers in Large Datasets
Edwin M. Knorr, Raymond T. Ng · 1998 · 1.5K citations
Finding Intensional Knowledge of Distance-Based Outliers
Edwin M. Knorr, Raymond T. Ng · 1999 · 422 citations
Computing, Data Science, Data Mining +12
Generalized Out-of-Distribution Detection: A Survey
Jingkang Yang, Kaiyang Zhou, Yixuan Li et al. · International Journal of Computer Vision · 2024 · 422 citations
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226
The Ohio State University
Columbus, United States
5 h-index
10 publications
894 citations

Harbin Institute of Technology
Harbin, China
5 h-index
13 publications
403 citations

University of Calabria
Rende, Italy
5 h-index
10 publications
1.1K citations
Nanjing University
Nanjing, China
5 h-index
9 publications
6K citations
Aristotle University of Thessaloniki
Thessaloniki, Greece
4 h-index
20 publications
1.6K citations
University of Tsukuba
Tsukuba, Japan
4 h-index
7 publications
131 citations

The University of Melbourne
Melbourne, Australia
3 h-index
9 publications
244 citations
Jain University
Bengaluru, India
3 h-index
4 publications
142 citations
1–8 of 226
Repository · 5 publications · 439 citations
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4 publications · 291 citations
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MSSP · 4 publications · 377 citations
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PVLDB · 3 publications · 302 citations
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TKDE · 3 publications · 197 citations
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