Point Outlier Detection

Point Outlier Detection is a type of outlier detection method that identifies individual data instances significantly deviating from the majority of the dataset based on their intrinsic properties, without reference to temporal or contextual dependencies.

461

Publications

40K

Citations

1.9K

Authors

702

Institutions

Publications per year

2017–2026

170

Authors

1.9K

  1. HK
    Hiroyuki Kitagawa

    University of Tsukuba, Japan

    6 publications · 959 citations

    6

    h-index

  2. CC
    Charų C. Aggarwal

    IBM (United States), United States

    6 publications · 1.8K citations

    6

    h-index

  3. LC
    Lei Cao

    Worcester Polytechnic Institute, United States

    4 publications · 193 citations

    4

    h-index

  4. BP
    Barnabás Póczos

    Carnegie Mellon University, United States

    4 publications · 889 citations

    4

    h-index

  5. JS
    Jeffrey S. Simonoff

    New York University, United States

    4 publications · 484 citations

    4

    h-index

1–5 of 1.9K

Venues

  1. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 12 publications · 2.1K citations

    12

    h-index

  2. 9

    h-index

  3. arXiv (Cornell University)

    Repository · 7 publications · 759 citations

    7

    h-index

  4. Journal of the American Statistical Association

    6 publications · 2.6K citations

    6

    h-index

  5. Pattern Recognition Letters

    6 publications · 165 citations

    6

    h-index

1–5