A comparative study of the \(K\)-means algorithm and the normal mixture model for clustering: bivariate homoscedastic case

DOI10.1016/j.jspi.2009.12.025zbMath1184.62112OpenAlexW2092327329MaRDI QIDQ963857

Dingxi Qiu

Publication date: 14 April 2010

Published in: Journal of Statistical Planning and Inference (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.jspi.2009.12.025

zbMATH Keywords

EM algorithmclusteringdata mining\(K\)-means algorithmmixture modelmisclassification rateelongationmixing proportion

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (3)

Geometry-inference based clustering heuristic: new \(k\)-means metric for Gaussian data and experimental proof of concept ⋮ Exploring data envelopment analysis for measuring collaborated innovation efficiency of small and medium-sized enterprises in Korea ⋮ Principal points for an allometric extension model

Uses Software

• ElemStatLearn

Cites Work

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• On principal points for location mixtures of spherically symmetric distributions
• A comparative study of the \(K\)-means algorithm and the normal mixture model for clustering: univariate case
• Statistical analysis of finite mixture distributions
• Unresolved Problems in Cluster Analysis
• -Means: A new generalized k-means clustering algorithm
• Estimation of Principal Points
• Estimating the components of a mixture of normal distributions

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