Good (K-means) clusterings are unique (up to small perturbations)

DOI10.1016/j.jmva.2018.12.008zbMath1432.62190OpenAlexW2916659903WikidataQ128421992 ScholiaQ128421992MaRDI QIDQ2274925

Marina Meilă

Publication date: 1 October 2019

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

Full work available at URL: https://doi.org/10.1016/j.jmva.2018.12.008

zbMATH Keywords

clusterabilityspectral clusteringcluster validationK-means clusteringmodel free

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Inference from stochastic processes and spectral analysis (62M15)

Related Items (1)

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Cites Work

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• Local equivalences of distances between clusterings -- a geometric perspective
• A spectral algorithm for learning mixture models
• Are Stable Instances Easy?
• A Sober Look at Clustering Stability
• The effectiveness of lloyd-type methods for the k-means problem
• Clustering under approximation stability
• Multi-way spectral partitioning and higher-order cheeger inequalities
• Learning Theory
• Learning Theory
• Partitioning Well-Clustered Graphs: Spectral Clustering Works!
• Finding Endogenously Formed Communities

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