Document Clustering

Document Clustering is a text analytics technique that groups unlabeled documents into coherent clusters based on semantic similarity, distinguishing itself from classification by operating without predefined labels.

7.4K

Publications

599.1K

Citations

14.9K

Authors

3.2K

Institutions

Publications per year

2017–2026

2K

Authors

14.9K

  1. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    51 publications · 2.8K citations

    33

    h-index

  2. WP
    Witold Pedrycz

    University of Alberta, Canada

    33 publications · 2.1K citations

    24

    h-index

  3. JC
    James C. Bezdek

    University of West Florida, United States

    26 publications · 13.2K citations

    21

    h-index

  4. JH
    Jiawei Han

    University of Illinois Urbana-Champaign, United States

    28 publications · 3.1K citations

    21

    h-index

  5. XL
    Xinwang Liu

    National University of Defense Technology, China

    28 publications · 1.9K citations

    21

    h-index

1–5 of 14.9K

Venues

  1. Pattern Recognition

    301 publications · 29.2K citations

    86

    h-index

  2. Expert Systems with Applications

    198 publications · 14.6K citations

    64

    h-index

  3. Information Sciences

    194 publications · 11.3K citations

    55

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 105 publications · 12.9K citations

    52

    h-index

  5. Knowledge-Based Systems

    134 publications · 8.4K citations

    50

    h-index

1–5