A feasible \(k\)-means kernel trick under non-Euclidean feature space

DOI10.34768/amcs-2020-0052zbMath1467.62089OpenAlexW3120583973MaRDI QIDQ2023581

S. T. Wierzchon, Mieczysław Kłopotek, Robert A. Kłopotek

Publication date: 3 May 2021

Published in: International Journal of Applied Mathematics and Computer Science (Search for Journal in Brave)

Full work available at URL: https://content.sciendo.com/view/journals/amcs/30/4/article-p703.xml

zbMATH Keywords

clusteringkernel methods\(k\)-meansGower/Legendre theoremnon-Euclidean feature space

Mathematics Subject Classification ID

Estimation in multivariate analysis (62H12) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (4)

On the discrepancy between Kleinberg's clustering axioms and \(k\)-means clustering algorithm behavior ⋮ A new clustering preserving transformation for \(k\)-means algorithm output ⋮ An outlier-robust neuro-fuzzy system for classification and regression ⋮ Fitting a Gaussian mixture model through the Gini index

Uses Software

• sedaR

Cites Work

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