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
Parents
4
Detecting Behavior Anomalies in Graphical User Interfaces
Vitalii Avdiienko, Konstantin Kuznetsov, Isabelle Rommelfanger et al. · 2017 · 41 citations
Computing, Design, Behavior Analysis +16
HD-KT: Advancing Robust Knowledge Tracing via Anomalous Learning Interaction Detection
Haiping Ma, Yong Yang, Chuan Qin et al. · 2024 · 25 citations
Interactive Continual Learning: Fast and Slow Thinking
Biqing Qi, Xinquan Chen, Junqi Gao et al. · 2024 · 17 citations
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30
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1
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1
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1
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1–5 of 30
12
University of Illinois Chicago
Chicago, United States
1 h-index
1 publications
39 citations

South China Agricultural University
Guangzhou, China
1 h-index
1 publications
24 citations
Tsinghua University
Beijing, China
1 h-index
1 publications
25 citations
Kerala Veterinary and Animal Sciences University
Kalpatta, India
1 h-index
1 publications
24 citations

Changsha University
Changsha, China
1 h-index
6 publications
144 citations

Johns Hopkins University
Baltimore, United States
1 h-index
1 publications
39 citations
Venus Medtech (China)
Hangzhou, China
1 h-index
1 publications
25 citations

Saarland University
Saarbrücken, Germany
1 h-index
6 publications
246 citations
1–8 of 12
1 publications · 39 citations
1
h-index
1 publications · 24 citations
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1–2