A \(k\)-median algorithm with running time independent of data size

DOI10.1023/B:MACH.0000033115.78247.f0zbMath1093.68635OpenAlexW2004931706MaRDI QIDQ703077

Liadan O'Callaghan, Adam Meyerson, Serge A. Plotkin

Publication date: 19 January 2005

Published in: Machine Learning (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1023/b:mach.0000033115.78247.f0

zbMATH Keywords

samplingClusteringsublinear

Mathematics Subject Classification ID

Nonnumerical algorithms (68W05) Learning and adaptive systems in artificial intelligence (68T05)

Related Items (5)

A framework for statistical clustering with constant time approximation algorithms for \(K\)-median and \(K\)-means clustering ⋮ A new efficient algorithm based on DC programming and DCA for clustering ⋮ Unnamed Item ⋮ Small space representations for metric min-sum \(k\)-clustering and their applications ⋮ Sublinear-time Algorithms

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