Anomaly Detection Visualization

Anomaly Detection Visualization is a visual analytics method that enables the identification and interpretation of rare or unexpected patterns in clinical and scientific data by integrating computational anomaly detection techniques with interactive visualization to support expert judgment and decision-making.

44

Publications

2K

Citations

155

Authors

57

Institutions

Publications per year

2017–2026

13

Authors

155

  1. MR
    Maria Riveiro

    University of Skövde, Sweden

    4 publications · 89 citations

    4

    h-index

  2. NC
    Nan Cao

    Tongji University, China

    3 publications · 369 citations

    3

    h-index

  3. GF
    Göran Falkman

    University of Skövde, Sweden

    3 publications · 76 citations

    3

    h-index

  4. YL
    Yu‐Ru Lin

    University of Pittsburgh, United States

    3 publications · 369 citations

    3

    h-index

  5. HQ
    Huamin Qu

    Hong Kong University of Science and Technology, Hong Kong SAR China

    2 publications · 72 citations

    2

    h-index

1–5 of 155

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 3 publications · 399 citations

    3

    h-index

  2. ACM Transactions on Interactive Intelligent Systems

    TIIS · 2 publications · 32 citations

    2

    h-index

  3. Expert Systems with Applications

    2 publications · 137 citations

    2

    h-index

  4. arXiv (Cornell University)

    Repository · 2 publications · 50 citations

    2

    h-index

  5. 1

    h-index

1–5