Outlier Detection

Outlier Detection is a data analysis technique in the field of data mining and meta-analysis that identifies individual data points significantly deviating from the expected or typical patterns within a dataset, distinguishing itself by focusing on anomalies that may indicate errors, rare events, or influential observations rather than systematic trends or central tendencies.

6.4K

Publications

469.1K

Citations

15.4K

Authors

3.5K

Institutions

Publications per year

2017–2026

2.8K

Authors

15.4K

  1. AC
    Anthony C. Atkinson

    London School of Economics and Political Science, United Kingdom

    25 publications · 3.6K citations

    22

    h-index

  2. AZ
    Arthur Zimek

    Ludwig-Maximilians-Universität München, Germany

    24 publications · 5.4K citations

    22

    h-index

  3. PJ
    Peter J. Rousseeuw

    University of Antwerp, Belgium

    23 publications · 10.9K citations

    19

    h-index

  4. GP
    Guansong Pang

    Singapore Management University, Singapore

    22 publications · 1.6K citations

    18

    h-index

  5. PF
    Peter Filzmoser

    TU Wien, Austria

    22 publications · 2.2K citations

    18

    h-index

1–5 of 15.4K

Venues

  1. Technometrics

    109 publications · 22.7K citations

    51

    h-index

  2. Journal of the American Statistical Association

    90 publications · 14.3K citations

    50

    h-index

  3. arXiv (Cornell University)

    Repository · 186 publications · 8.4K citations

    41

    h-index

  4. Expert Systems with Applications

    78 publications · 3.8K citations

    38

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 47 publications · 7.3K citations

    37

    h-index

1–5