Refined Mode-Clustering via the Gradient of Slope

Kunhui Zhang and Yen-Chi Chen
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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

Stats, 2021, vol. 4, issue 2, 1-23

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.

Keywords: clustering; mode-clustering; gradient descent; two-sample test (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2021
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