On the added value of bootstrap analysis for \(K\)-means clustering

DOI10.1007/s00357-015-9178-yzbMath1335.62099OpenAlexW2214474384MaRDI QIDQ269548

Iven van Mechelen, Eva Ceulemans, Douglas Steinley, Joeri Hofmans

Publication date: 19 April 2016

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00357-015-9178-y

zbMATH Keywords

clusteringbootstrapping\(K\)-means

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Nonparametric tolerance and confidence regions (62G15) Nonparametric statistical resampling methods (62G09)

Related Items (3)

On strategies to fix degenerate \(k\)-means solutions ⋮ Modeling differences in the dimensionality of multiblock data by means of clusterwise simultaneous component analysis ⋮ Initializing \(k\)-means clustering by bootstrap and data depth

Uses Software

• bootstrap
• bootlib

Cites Work

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• Optimising \(k\)-means clustering results with standard software packages
• Cluster-wise assessment of cluster stability
• A central limit theorem for k-means clustering
• Bootstrap methods: another look at the jackknife
• Consensus clustering: A resampling-based method for class discovery and visualization of gene expression microarray data
• Initializing \(K\)-means batch clustering: A critical evaluation of several techniques
• Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
• Algorithms for the Assignment and Transportation Problems
• On confidence regions in canonical variate analysis
• BOOTSTRAPPING K-MEANS CLUSTERING
• Finding Groups in Data
• Asymptotic behaviour of classification maximum likelihood estimates
• Finite mixture models
• Bootstrapping for Significance of Compact Clusters in Multidimensional Datasets

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