Modal Clustering

Modal Clustering is a clustering method that partitions data points into groups based on the local maxima of a probability density function estimated from the data, where each cluster corresponds to the basin of attraction of a mode in the density landscape.

35

Publications

1.1K

Citations

99

Authors

56

Institutions

Publications per year

2017–2026

21

Variants

Mode-seeking Clustering

Authors

99

  1. JE
    José E. Chacón

    Universidad de Extremadura, Spain

    3 publications · 69 citations

    3

    h-index

  2. JZ
    Jice Zeng

    University of Michigan–Dearborn, United States

    2 publications · 45 citations

    2

    h-index

  3. XC
    Xiaochun Cao

    Chinese Academy of Sciences, China

    2 publications · 41 citations

    2

    h-index

  4. RB
    Ron Bekkerman

    University of Massachusetts Amherst, United States

    2 publications · 89 citations

    2

    h-index

  5. YH
    Young Hoon Kim

    University of Louisville, United States

    2 publications · 45 citations

    2

    h-index

1–5 of 99

Venues

  1. arXiv (Cornell University)

    Repository · 4 publications · 73 citations

    4

    h-index

  2. Journal of Civil Structural Health Monitoring

    3 publications · 80 citations

    3

    h-index

  3. Advances in Data Analysis and Classification

    2 publications · 43 citations

    2

    h-index

  4. 1

    h-index

  5. Computational Statistics & Data Analysis

    CSDA · 1 publications · 11 citations

    1

    h-index

1–5