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Hierarchical clustering with optimal transport

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  • Chakraborty, Saptarshi
  • Paul, Debolina
  • Das, Swagatam

Abstract

Optimal Transport (OT) distances result in a powerful technique to compare the probability distributions. Defining a similarity measure between clusters has been an open problem in Statistics. This paper introduces a hierarchical clustering algorithm using the OT based distance measures and analyzes the performance of the proposed algorithm on standard datasets with respect to the existing and popular hierarchical clustering methods.

Suggested Citation

  • Chakraborty, Saptarshi & Paul, Debolina & Das, Swagatam, 2020. "Hierarchical clustering with optimal transport," Statistics & Probability Letters, Elsevier, vol. 163(C).
    • Handle: RePEc:eee:stapro:v:163:y:2020:i:c:s0167715220300845
    DOI: 10.1016/j.spl.2020.108781
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    References listed on IDEAS

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    1. Witten, Daniela M. & Tibshirani, Robert, 2010. "A Framework for Feature Selection in Clustering," Journal of the American Statistical Association, American Statistical Association, vol. 105(490), pages 713-726.
    2. Junhui Wang, 2010. "Consistent selection of the number of clusters via crossvalidation," Biometrika, Biometrika Trust, vol. 97(4), pages 893-904.
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    2. Yihang Xu & Junxi Wu & Guoyan Zhao & Meng Wang & Xing Zhou, 2024. "II-LA-KM: Improved Initialization of a Learning-Augmented Clustering Algorithm for Effective Rock Discontinuity Grouping," Mathematics, MDPI, vol. 12(20), pages 1-17, October.

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