On the quality of \(k\)-means clustering based on grouped data

DOI10.1016/J.JSPI.2009.05.021zbMath1169.62059OpenAlexW2063967481MaRDI QIDQ840740

Meelis Käärik, Kalev Pärna

Publication date: 14 September 2009

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.2009.05.021

zbMATH Keywords

Lloyd's algorithmloss-functionVoronoi partitions

Mathematics Subject Classification ID

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

Related Items (1)

Geometry-inference based clustering heuristic: new \(k\)-means metric for Gaussian data and experimental proof of concept

Cites Work

• Unnamed Item
• The strong law of large numbers for k-means and best possible nets of Banach valued random variables
• Strong consistency of k-means clustering
• Probabilistic models in cluster analysis
• Foundations of quantization for probability distributions
• Principal points
• Quantization
• Least squares quantization in PCM

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