Clustering-based Retrieval

Clustering-Based Retrieval is a vector space retrieval method that organizes documents into clusters based on similarity metrics, and retrieves relevant results by identifying the cluster most closely associated with the query vector.

299

Publications

19.2K

Citations

693

Authors

301

Institutions

Publications per year

2017–2026

28

Authors

693

  1. OK
    Oren Kurland

    Technion – Israel Institute of Technology, Israel

    11 publications · 398 citations

    11

    h-index

  2. PW
    Peter Willett

    University of Sheffield, United Kingdom

    6 publications · 377 citations

    6

    h-index

  3. WM
    Wei‐Ying Ma

    Microsoft Research Asia (China), China

    4 publications · 708 citations

    4

    h-index

  4. OE
    Oren Etzioni

    University of Washington, United States

    4 publications · 1.9K citations

    4

    h-index

  5. SM
    Simone Marinai

    University of Florence, Italy

    4 publications · 73 citations

    4

    h-index

1–5 of 693

Venues

  1. Information Processing & Management

    14 publications · 622 citations

    13

    h-index

  2. Journal of the American Society for Information Science

    JASIS · 7 publications · 398 citations

    7

    h-index

  3. ACM Transactions on Database Systems

    TODS · 4 publications · 273 citations

    4

    h-index

  4. Expert Systems with Applications

    4 publications · 159 citations

    4

    h-index

  5. Information Retrieval

    4 publications · 78 citations

    4

    h-index

1–5