Kernel-based Clustering

Kernel-Based Clustering is a spatial clustering method that employs kernel functions to map input data into a higher-dimensional feature space, where non-linearly separable clusters in the original space become linearly separable through implicit density estimation and mode detection in the transformed space.

459

Publications

29.8K

Citations

1.2K

Authors

533

Institutions

Publications per year

2017–2026

194

Authors

1.2K

  1. XL
    Xinwang Liu

    National University of Defense Technology, China

    26 publications · 1.7K citations

    21

    h-index

  2. EZ
    En Zhu

    National University of Defense Technology, China

    15 publications · 1.2K citations

    15

    h-index

  3. JA
    Johan A. K. Suykens

    KU Leuven, Belgium

    12 publications · 642 citations

    11

    h-index

  4. ZR
    Zhenwen Ren

    Southwest University of Science and Technology, China

    10 publications · 626 citations

    10

    h-index

  5. JY
    Jianping Yin

    National University of Defense Technology, China

    9 publications · 878 citations

    9

    h-index

1–5 of 1.2K

Venues

  1. Pattern Recognition

    19 publications · 1.9K citations

    15

    h-index

  2. Information Sciences

    17 publications · 866 citations

    15

    h-index

  3. Neurocomputing

    13 publications · 544 citations

    13

    h-index

  4. Proceedings of the AAAI Conference on Artificial Intelligence

    Conference · 13 publications · 841 citations

    13

    h-index

  5. IEEE Transactions on Pattern Analysis and Machine Intelligence

    TPAMI · 10 publications · 1.5K citations

    10

    h-index

1–5