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

Authors

633

  1. FA
    Fabrizio Angiulli

    University of Calabria, Italy

    11 publications · 1.2K citations

    11

    h-index

  2. SP
    Srinivasan Parthasarathy

    The Ohio State University, United States

    5 publications · 500 citations

    5

    h-index

  3. HK
    Hiroyuki Kitagawa

    University of Tsukuba, Japan

    5 publications · 927 citations

    5

    h-index

  4. RT
    Raymond T. Ng

    University of British Columbia, Canada

    5 publications · 3.2K citations

    5

    h-index

  5. SB
    Stefano Basta

    Institute for High Performance Computing and Networking, Italy

    5 publications · 418 citations

    5

    h-index

1–5 of 633

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 8 publications · 1.3K citations

    8

    h-index

  2. IEEE Access

    8 publications · 738 citations

    7

    h-index

  3. 4

    h-index

  4. 4

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  5. Pattern Recognition

    4 publications · 230 citations

    4

    h-index

1–5