Graph-based Text Clustering

Graph-Based Text Clustering is a text clustering approach that models documents and their relationships as a graph structure, where nodes represent documents or textual units and edges encode semantic or similarity relations, and applies graph partitioning or community detection algorithms to identify clusters.

106

Publications

6.5K

Citations

293

Authors

161

Institutions

Publications per year

2017–2026

24

Authors

293

  1. AK
    Asit Kumar Das

    Indian Institute of Engineering Science and Technology, Shibpur, India

    3 publications · 116 citations

    3

    h-index

  2. PS
    Philip S. Yu

    University of Illinois Chicago, United States

    3 publications · 225 citations

    3

    h-index

  3. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    3 publications · 602 citations

    3

    h-index

  4. MN
    Mohamed Nadif

    Université Paris Cité, France

    2 publications · 80 citations

    2

    h-index

  5. XP
    Xiaobing Pei

    Huazhong University of Science and Technology, China

    2 publications · 114 citations

    2

    h-index

1–5 of 293

Venues

  1. Information Sciences

    2 publications · 33 citations

    2

    h-index

  2. Pattern Recognition

    2 publications · 205 citations

    2

    h-index

  3. Knowledge-Based Systems

    2 publications · 78 citations

    2

    h-index

  4. ACM Transactions on Knowledge Discovery from Data

    TKDD · 2 publications · 203 citations

    2

    h-index

  5. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 446 citations

    2

    h-index

1–5