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Hierarchical clustering with optimal transport

Author

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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. Junhui Wang, 2010. "Consistent selection of the number of clusters via crossvalidation," Biometrika, Biometrika Trust, vol. 97(4), pages 893-904.
    2. 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.
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    Cited by:

    1. Yutaka Iwakami & Hironori Takuma & Motoi Iwashita, 2020. "Improving Matching Process with Expanding and Classifying Criterial Keywords leveraging Word Embedding and Hierarchical Clustering Methods," The Review of Socionetwork Strategies, Springer, vol. 14(2), pages 193-204, October.

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