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Stats, Volume 4, Issue 2

2021 June - 16 articles

Cover Story: 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 the connectivity of the underlying distribution. Excluding the regions around the mode, the connectivity refers to the regions with relatively high density compared to other regions. We improve the usual mode-clustering method by (1) adding additional clusters that can further partition the entire sample space, and (2) assigning an attribute label to each cluster. 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 for illustration. Finally, we derive both statistical and computational guarantees of the proposed method. View this paper
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Stats - ISSN 2571-905X
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