A FAST k-MEANS IMPLEMENTATION USING CORESETS

DOI10.1142/S0218195908002787zbMath1182.65034MaRDI QIDQ3604141

Gereon Frahling, Christian Sohler

Publication date: 24 February 2009

Published in: International Journal of Computational Geometry & Applications (Search for Journal in Brave)

zbMATH Keywords

computational geometryapproximation algorithmsclustering algorithmsalgorithm engineering

Mathematics Subject Classification ID

Numerical aspects of computer graphics, image analysis, and computational geometry (65D18)

Related Items (4)

A strong coreset algorithm to accelerate OPF as a graph-based machine learning in large-scale problems ⋮ Clustering-based ensembles for one-class classification ⋮ Compressive statistical learning with random feature moments ⋮ Approximating Spectral Clustering via Sampling: A Review

Cites Work

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• Optimal time bounds for approximate clustering
• Smaller coresets for \(k\)-median and \(k\)-means clustering
• On approximate geometric \(k\)-clustering
• Clustering motion
• Faster core-set constructions and data-stream algorithms in fixed dimensions
• Approximating extent measures of points
• Sublinear‐time approximation algorithms for clustering via random sampling
• Least squares quantization in PCM

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