Unsupervised Outlier Detection

Unsupervised Outlier Detection is an outlier detection approach that identifies anomalies in data without relying on labeled instances, distinguishing itself from supervised and semi-supervised methods by operating solely on unlabeled datasets under the assumption that outliers deviate significantly from the majority pattern.

329

Publications

22.6K

Citations

1.1K

Authors

489

Institutions

Publications per year

2017–2026

227

Authors

1.1K

  1. AZ
    Arthur Zimek

    Ludwig-Maximilians-Universität München, Germany

    10 publications · 3.1K citations

    10

    h-index

  2. JS
    Jörg Sander

    University of Alberta, Canada

    8 publications · 2.2K citations

    8

    h-index

  3. RJ
    Ricardo J. G. B. Campello

    Universidade de São Paulo, Brazil

    8 publications · 2.2K citations

    8

    h-index

  4. YZ
    Yue Zhao

    Carnegie Mellon University, United States

    6 publications · 528 citations

    6

    h-index

  5. GP
    Guansong Pang

    Singapore Management University, Singapore

    6 publications · 427 citations

    6

    h-index

1–5 of 1.1K

Venues

  1. arXiv (Cornell University)

    Repository · 20 publications · 1.4K citations

    15

    h-index

  2. Expert Systems with Applications

    7 publications · 208 citations

    7

    h-index

  3. Sensors

    7 publications · 191 citations

    7

    h-index

  4. Knowledge-Based Systems

    6 publications · 239 citations

    6

    h-index

  5. Data Mining and Knowledge Discovery

    6 publications · 1.5K citations

    6

    h-index

1–5