Cluster Exploration

Cluster Exploration is a methodological framework within cluster visualization and network exploration that systematically identifies, characterizes, and interprets coherent substructures in complex networks by integrating topological analysis with semantic labeling to facilitate meaningful, human-centric understanding of clustered data regions.

65

Publications

3.6K

Citations

245

Authors

124

Institutions

Publications per year

2017–2026

14

Authors

245

  1. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    4 publications · 456 citations

    4

    h-index

  2. XY
    Xifeng Yan

    University of California, Santa Barbara, United States

    3 publications · 414 citations

    3

    h-index

  3. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    3 publications · 414 citations

    3

    h-index

  4. YS
    Yizhou Sun

    University of California, Los Angeles, United States

    3 publications · 414 citations

    3

    h-index

  5. BN
    Brandon Norick

    University of Illinois Urbana-Champaign, United States

    3 publications · 414 citations

    3

    h-index

1–5 of 245

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 6 publications · 331 citations

    6

    h-index

  2. Internet Mathematics

    2 publications · 83 citations

    2

    h-index

  3. Knowledge-Based Systems

    2 publications · 27 citations

    2

    h-index

  4. ACM Transactions on Knowledge Discovery from Data

    TKDD · 2 publications · 203 citations

    2

    h-index

  5. Neural Networks

    1 publications · 24 citations

    1

    h-index

1–5