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Refined Mode-Clustering via the Gradient of Slope

Author

Listed:
  • Kunhui Zhang

    (Department of Statistics, University of Washington, Seattle, WA 98195, USA)

  • Yen-Chi Chen

    (Department of Statistics, University of Washington, Seattle, WA 98195, USA)

Abstract

In this paper, we propose a new clustering method inspired by mode-clustering that not only finds clusters, but also assigns each cluster with an attribute label. Clusters obtained from our method show connectivity of the underlying distribution. We also design a local two-sample test based on the clustering result that has more power than a conventional method. We apply our method to the Astronomy and GvHD data and show that our method finds meaningful clusters. We also derive the statistical and computational theory of our method.

Suggested Citation

  • Kunhui Zhang & Yen-Chi Chen, 2021. "Refined Mode-Clustering via the Gradient of Slope," Stats, MDPI, vol. 4(2), pages 1-23, June.
    • Handle: RePEc:gam:jstats:v:4:y:2021:i:2:p:30-508:d:567065
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    References listed on IDEAS

    as
    1. Scrucca, Luca, 2016. "Identifying connected components in Gaussian finite mixture models for clustering," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 5-17.
    2. Christopher R. Genovese & Marco Perone-Pacifico & Isabella Verdinelli & Larry Wasserman, 2012. "The Geometry of Nonparametric Filament Estimation," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(498), pages 788-799, June.
    3. Vieu, Philippe, 1996. "A note on density mode estimation," Statistics & Probability Letters, Elsevier, vol. 26(4), pages 297-307, March.
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