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

Authors

515

  1. SP
    Srinivasan Parthasarathy

    The Ohio State University, United States

    5 publications · 500 citations

    5

    h-index

  2. FA
    Fabrizio Angiulli

    University of Calabria, Italy

    5 publications · 439 citations

    5

    h-index

  3. AN
    Apostolos N. Papadopoulos

    Aristotle University of Thessaloniki, Greece

    4 publications · 310 citations

    4

    h-index

  4. AG
    Anastasios Gounaris

    Aristotle University of Thessaloniki, Greece

    4 publications · 310 citations

    4

    h-index

  5. HK
    Hiroyuki Kitagawa

    University of Tsukuba, Japan

    4 publications · 76 citations

    4

    h-index

1–5 of 515

Venues

  1. arXiv (Cornell University)

    Repository · 5 publications · 439 citations

    5

    h-index

  2. IEEE Access

    4 publications · 291 citations

    4

    h-index

  3. Mechanical Systems and Signal Processing

    MSSP · 4 publications · 377 citations

    4

    h-index

  4. Proceedings of the VLDB Endowment

    PVLDB · 3 publications · 302 citations

    3

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 3 publications · 197 citations

    3

    h-index

1–5