Outlier Detection Interaction

Outlier Detection Interaction is an interactive method within data exploration systems that enables users to identify, analyze, and refine the treatment of anomalous data points through iterative human-in-the-loop processes, distinguished by its reliance on user feedback to dynamically adjust detection criteria and interpretations.

4

Publications

129

Citations

30

Authors

12

Institutions

Publications per year

2017–2026

3

Authors

30

  1. XY
    Xiaoshan Yu

    Anhui University, China

    1 publications · 25 citations

    1

    h-index

  2. HY
    Haoxin Ye

    University of British Columbia, Canada

    1 publications · 39 citations

    1

    h-index

  3. IR
    Isabelle Rommelfanger

    Saarland University, Germany

    1 publications · 41 citations

    1

    h-index

  4. HZ
    Hengshu Zhu

    Baidu (China), China

    1 publications · 25 citations

    1

    h-index

  5. ER
    Edward R. Grant

    Purdue University West Lafayette, United States

    1 publications · 39 citations

    1

    h-index

1–5 of 30

Venues

  1. ACS Nano

    1 publications · 39 citations

    1

    h-index

  2. The Analyst

    1 publications · 24 citations

    1

    h-index

1–2