Multi-view Document Clustering

Multi-View Document Clustering is a document clustering approach that integrates multiple heterogeneous representations or views of documents—such as textual content, citation networks, or author information—by jointly modeling complementary structural and semantic information across views to produce a unified clustering structure.

36

Publications

3.3K

Citations

134

Authors

63

Institutions

Publications per year

2017–2026

24

Authors

134

  1. WZ
    Wensheng Zhang

    Chinese Academy of Sciences, China

    2 publications · 496 citations

    2

    h-index

  2. TL
    Tianrui Li

    Southwest Jiaotong University, China

    2 publications · 129 citations

    2

    h-index

  3. FN
    Feiping Nie

    Northwestern Polytechnical University, China

    2 publications · 103 citations

    2

    h-index

  4. BC
    Badong Chen

    Xi'an Jiaotong University, China

    2 publications · 103 citations

    2

    h-index

  5. KZ
    Kun Zhan

    Lanzhou University, China

    2 publications · 85 citations

    2

    h-index

1–5 of 134

Venues

  1. Information Sciences

    4 publications · 175 citations

    4

    h-index

  2. Neurocomputing

    4 publications · 360 citations

    4

    h-index

  3. Knowledge-Based Systems

    3 publications · 153 citations

    3

    h-index

  4. IEEE Transactions on Knowledge and Data Engineering

    TKDE · 2 publications · 259 citations

    2

    h-index

  5. Expert Systems with Applications

    2 publications · 54 citations

    2

    h-index

1–5