Asymptotic properties of hierarchical clustering in high-dimensional settings

DOI10.1016/j.jmva.2023.105251OpenAlexW4388664589MaRDI QIDQ6183699

Kento Egashira, Makoto Aoshima, Kazuyoshi Yata

Publication date: 4 January 2024

Published in: Journal of Multivariate Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jmva.2023.105251

zbMATH Keywords

clustering behaviormulticlasshigh-dimension low-sample-sizeWard's linkage function

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Multivariate analysis (62Hxx)

Cites Work

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• Statistical Significance of Clustering for High-Dimension, Low–Sample Size Data
• Two-Stage Procedures for High-Dimensional Data
• Geometric consistency of principal component scores for high‐dimensional mixture models and its application
• Clustering high dimension, low sample size data using the maximal data piling distance
• Statistical Significance for Hierarchical Clustering

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