Kernel-based Cluster Detection

Kernel-Based Cluster Detection is a cluster detection method that employs kernel functions to map data into higher-dimensional spaces where clusters are more discernible, distinguishing itself from other methods by leveraging kernel-induced similarity measures to identify non-linearly separable groupings.

71

Publications

4K

Citations

222

Authors

135

Institutions

Publications per year

2017–2026

24

Authors

222

  1. RL
    Rocco Langone

    KU Leuven, Belgium

    2 publications · 69 citations

    2

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  2. AD
    Anne Denton

    North Dakota State University, United States

    2 publications · 57 citations

    2

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  3. JA
    Johan A. K. Suykens

    KU Leuven, Belgium

    2 publications · 69 citations

    2

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  4. ZZ
    Zhou Zhou

    University of Toronto, Canada

    2 publications · 82 citations

    2

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  5. RM
    Raghvendra Mall

    Hamad bin Khalifa University, Qatar

    2 publications · 69 citations

    2

    h-index

1–5 of 222

Venues

  1. Information Sciences

    2 publications · 184 citations

    2

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  2. Pattern Recognition Letters

    2 publications · 103 citations

    2

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  3. 2

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  4. Expert Systems with Applications

    2 publications · 106 citations

    2

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  5. 2

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1–5