Contextual Outlier Detection

Contextual Outlier Detection is an outlier detection paradigm that identifies anomalies based on their deviation from the expected behavior within a specific context, as defined by contextual attributes, distinguishing it from global or collective outlier detection methods by incorporating situational dependency in the definition of normality.

54

Publications

2.3K

Citations

178

Authors

86

Institutions

Publications per year

2017–2026

28

Variants

Context-dependent Outlier Detection

Authors

178

  1. JZ
    Jifu Zhang

    Taiyuan University of Science and Technology, China

    3 publications · 76 citations

    3

    h-index

  2. XQ
    Xiao Qin

    Auburn University, United States

    3 publications · 76 citations

    3

    h-index

  3. LC
    Lei Cao

    Worcester Polytechnic Institute, United States

    2 publications · 69 citations

    2

    h-index

  4. XH
    Xia Hu

    Texas A&M University, United States

    2 publications · 74 citations

    2

    h-index

  5. SM
    Samuel Madden

    Massachusetts Institute of Technology, United States

    2 publications · 69 citations

    2

    h-index

1–5 of 178

Venues

  1. Data Mining and Knowledge Discovery

    2 publications · 315 citations

    2

    h-index

  2. arXiv (Cornell University)

    Repository · 2 publications · 23 citations

    2

    h-index

  3. ACM Transactions on Intelligent Systems and Technology

    TIST · 2 publications · 27 citations

    2

    h-index

  4. Journal of Sensor and Actuator Networks

    JSAN · 1 publications · 84 citations

    1

    h-index

  5. 1

    h-index

1–5