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

Publications

17.7K

Citations

531

Authors

252

Institutions

Publications per year

2017–2026

92

Authors

531

  1. AZ
    Arthur Zimek

    Ludwig-Maximilians-Universität München, Germany

    5 publications · 2K citations

    5

    h-index

  2. CP
    Claudia Plant

    Ludwig-Maximilians-Universität München, Germany

    4 publications · 72 citations

    4

    h-index

  3. AG
    Anjana Gosain

    Guru Gobind Singh Indraprastha University, India

    3 publications · 47 citations

    3

    h-index

  4. JS
    Jörg Sander

    University of Alberta, Canada

    3 publications · 1.8K citations

    3

    h-index

  5. CB
    Christian Böhm

    Ludwig-Maximilians-Universität München, Germany

    3 publications · 48 citations

    3

    h-index

1–5 of 531

Venues

  1. IEEE Access

    12 publications · 939 citations

    11

    h-index

  2. arXiv (Cornell University)

    Repository · 4 publications · 370 citations

    4

    h-index

  3. Expert Systems with Applications

    3 publications · 104 citations

    3

    h-index

  4. ACM Transactions on Knowledge Discovery from Data

    TKDD · 3 publications · 1K citations

    3

    h-index

  5. Information Sciences

    3 publications · 199 citations

    3

    h-index

1–5