Improving K-means method via shrinkage estimation and LVQ algorithm

DOI10.1080/03610918.2019.1620274zbMath1497.62153OpenAlexW2947894340MaRDI QIDQ5082771

Hui Wang, Zhouping Li

Publication date: 21 June 2022

Published in: Communications in Statistics - Simulation and Computation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/03610918.2019.1620274

zbMATH Keywords

clusteringsupervised learningshrinkage estimatorunsupervised learning\(K\)-meansLVQ

Mathematics Subject Classification ID

Ridge regression; shrinkage estimators (Lasso) (62J07) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Learning and adaptive systems in artificial intelligence (68T05)

Uses Software

• UCI-ml
• k-means++

Cites Work

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• Minimax estimators of the mean of a multivariate normal distribution
• Trimmed \(k\)-means: An attempt to robustify quantizers
• The learning vector quantization algorithm applied to automatic text classification tasks
• James-Stein shrinkage to improve \(k\)-means cluster analysis
• Robust Linear Clustering
• Robust Estimation in the Normal Mixture Model Based on Robust Clustering
• Semi-supervised k-means++

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