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  • 标题:Quantile-based clustering
  • 本地全文:下载
  • 作者:Christian Hennig ; Cinzia Viroli ; Laura Anderlucci
  • 期刊名称:Electronic Journal of Statistics
  • 印刷版ISSN:1935-7524
  • 出版年度:2019
  • 卷号:13
  • 期号:2
  • 页码:4849-4883
  • DOI:10.1214/19-EJS1640
  • 语种:English
  • 出版社:Institute of Mathematical Statistics
  • 摘要:A new cluster analysis method, $K$-quantiles clustering, is introduced. $K$-quantiles clustering can be computed by a simple greedy algorithm in the style of the classical Lloyd’s algorithm for $K$-means. It can be applied to large and high-dimensional datasets. It allows for within-cluster skewness and internal variable scaling based on within-cluster variation. Different versions allow for different levels of parsimony and computational efficiency. Although $K$-quantiles clustering is conceived as nonparametric, it can be connected to a fixed partition model of generalized asymmetric Laplace-distributions. The consistency of $K$-quantiles clustering is proved, and it is shown that $K$-quantiles clusters correspond to well separated mixture components in a nonparametric mixture. In a simulation, $K$-quantiles clustering is compared with a number of popular clustering methods with good results. A high-dimensional microarray dataset is clustered by $K$-quantiles.
  • 关键词:Fixed partition model; quantile discrepancy; high dimensional clustering; nonparametric mixture
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