Trimmed best \(k\)-nets: A robustified version of an \(L_{\infty}\)-based clustering method

DOI10.1016/S0167-7152(97)00087-4zbMath0894.62078OpenAlexW1521935516MaRDI QIDQ1382213

Alfonso Gordaliza, Carlos Matrán, Juan Antonio Cuesta-Albertos

Publication date: 6 May 1998

Published in: Statistics \& Probability Letters (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/s0167-7152(97)00087-4

zbMATH Keywords

robustnessclusteringk-meansbest k-netsbridging objectstrimmed best k-netstrimmed k-means

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Robustness and adaptive procedures (parametric inference) (62F35) Strong limit theorems (60F15)

Related Items (3)

A review of robust clustering methods ⋮ On the asymptotics of trimmed best \(k\)-nets ⋮ A central limit theorem for multivariate generalized trimmed \(k\)-means

Uses Software

• clusfind

Cites Work

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• On the breakdown point of multivariate location estimators based on trimming procedures
• The strong law of large numbers for k-means and best possible nets of Banach valued random variables
• Best approximations to random variables based on trimming procedures
• Trimmed \(k\)-means: An attempt to robustify quantizers
• Least Median of Squares Regression
• Finding Groups in Data
• Optimum Distribution of Switching Centers in a Communication Network and Some Related Graph Theoretic Problems
• The best possible net and the best possible cross-section of a set in a normed space
• Convergence of stochastic processes

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