Density-based Outlier Detection
Density-Based Outlier Detection is an outlier detection approach that identifies anomalies by distinguishing data points in regions of low density relative to their neighbors, thereby differentiating outliers based on local density deviations within the data space.
188
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17.7K
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531
Authors
252
Institutions
Publications per year
2017–2026
92
188
Clustering by fast search and find of density peaks
Álex Rodríguez, Alessandro Laio · Science · 2014 · 5K citations
Computing, Astronomy, Bioinformatics +13
Hans‐Peter Kriegel, Peer Kröger, Jörg Sander et al. · Wiley Interdisciplinary Reviews Data Mining and Knowledge Discovery · 2011 · 810 citations
DBSCAN: Past, present and future
Saif Ur Rehman, Simon Fong, S. Sarasvady · 2014 · 591 citations
Progress in Outlier Detection Techniques: A Survey
Hongzhi Wang, Mohamed Jaward Bah, Mohamed Hammad · IEEE Access · 2019 · 508 citations · Full text
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Ludwig-Maximilians-Universität München
Munich, Germany
5 h-index
9 publications
3.5K citations

Xi'an Jiaotong University
Xi'an, China
5 h-index
16 publications
599 citations

University of Alberta
Edmonton, Canada
5 h-index
7 publications
2.8K citations
Texas A&M University
College Station, United States
4 h-index
6 publications
228 citations
Tokyo Institute of Technology
Tokyo, Japan
4 h-index
5 publications
269 citations
Jilin University
Changchun, China
4 h-index
6 publications
138 citations

Beijing University of Posts and Telecommunications
Beijing, China
4 h-index
19 publications
752 citations

Nanjing University of Aeronautics and Astronautics
Nanjing, China
4 h-index
12 publications
515 citations
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12 publications · 939 citations
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Repository · 4 publications · 370 citations
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3 publications · 104 citations
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TKDD · 3 publications · 1K citations
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3 publications · 199 citations
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