Interactive Clustering

Interactive Clustering is a data clustering method that distinguishes itself by incorporating real-time human feedback and iterative refinement into the clustering process to dynamically guide the formation and adjustment of clusters according to user-defined criteria and domain knowledge.

74

Publications

3.2K

Citations

222

Authors

109

Institutions

Publications per year

2017–2026

17

Authors

222

  1. EM
    Evangelos Milios

    Dalhousie University, Canada

    4 publications · 83 citations

    4

    h-index

  2. BO
    Benoît Otjacques

    Luxembourg Institute of Science and Technology, Luxembourg

    3 publications · 67 citations

    3

    h-index

  3. MR
    Michael R. Berthold

    University of Konstanz, Germany

    3 publications · 53 citations

    3

    h-index

  4. BW
    Bernd Wiswedel

    University of Konstanz, Germany

    3 publications · 53 citations

    3

    h-index

  5. ES
    Ehsan Sherkat

    Dalhousie University, Canada

    2 publications · 42 citations

    2

    h-index

1–5 of 222

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 93 citations

    4

    h-index

  2. Pattern Recognition Letters

    3 publications · 63 citations

    3

    h-index

  3. Intelligent Data Analysis

    IDA · 3 publications · 50 citations

    3

    h-index

  4. BMC Bioinformatics

    2 publications · 243 citations

    2

    h-index

  5. Pattern Recognition

    2 publications · 87 citations

    2

    h-index

1–5