Data Reduction For Visualization

Data Reduction for Visualization is a systematic method within visual computing that reduces the volume or complexity of datasets while preserving salient features relevant to visualization, thereby enabling efficient rendering and interactive exploration of large-scale data.

46

Publications

2.5K

Citations

144

Authors

85

Institutions

Publications per year

2017–2026

15

Authors

144

  1. SL
    Shaomeng Li

    NSF National Center for Atmospheric Research, United States

    2 publications · 92 citations

    2

    h-index

  2. KM
    Kwan‐Liu Ma

    University of California, Davis, United States

    2 publications · 142 citations

    2

    h-index

  3. HC
    Hank Childs

    Lawrence Berkeley National Laboratory, United States

    2 publications · 92 citations

    2

    h-index

  4. AP
    Alex Pang

    University of California, Santa Cruz, United States

    2 publications · 27 citations

    2

    h-index

  5. SD
    Suzana Djurcilov

    University of California, Santa Cruz, United States

    2 publications · 27 citations

    2

    h-index

1–5 of 144

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 5 publications · 667 citations

    5

    h-index

  2. Computer Graphics Forum

    4 publications · 439 citations

    4

    h-index

  3. Neural Computing and Applications

    1 publications · 39 citations

    1

    h-index

  4. Nature Methods

    1 publications · 58 citations

    1

    h-index

  5. Biomedical Signal Processing and Control

    1 publications · 15 citations

    1

    h-index

1–5