Topic-based Document Clustering

Topic-Based Document Clustering is a document clustering approach that organizes documents into groups based on shared thematic content derived from latent topics, distinguishing itself from other clustering methods by explicitly leveraging topic models to represent and cluster documents according to their semantic themes.

111

Publications

6.5K

Citations

318

Authors

148

Institutions

Publications per year

2017–2026

46

Authors

318

  1. BT
    Bart Thijs

    KU Leuven, Belgium

    2 publications · 157 citations

    2

    h-index

  2. JL
    Jianxun Liu

    Hunan University of Science and Technology, China

    2 publications · 89 citations

    2

    h-index

  3. EL
    E. Laxmi Lydia

    International Institute of Information Technology, India

    2 publications · 30 citations

    2

    h-index

  4. WG
    Wolfgang Glänzel

    KU Leuven, Belgium

    2 publications · 157 citations

    2

    h-index

  5. JA
    Juan Antonio Lossio-Ventura

    Stanford University, United States

    2 publications · 74 citations

    2

    h-index

1–5 of 318

Venues

  1. Scientometrics

    4 publications · 411 citations

    4

    h-index

  2. Information Sciences

    3 publications · 64 citations

    3

    h-index

  3. Expert Systems with Applications

    2 publications · 85 citations

    2

    h-index

  4. International Journal of Cloud Computing

    IJCC · 2 publications · 30 citations

    2

    h-index

  5. Knowledge and Information Systems

    1 publications · 56 citations

    1

    h-index

1–5