EFFICIENT APPROXIMATION ALGORITHMS FOR PAIRWISE DATA CLUSTERING AND APPLICATIONS

DOI10.1142/S0218195904001378zbMath1093.68073OpenAlexW2105630880MaRDI QIDQ4818598

Steven R. Schmid, Danny Z. Chen, J. J. Mason, Xiao Dong Wu

Publication date: 29 September 2004

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

Full work available at URL: https://doi.org/10.1142/s0218195904001378

zbMATH Keywords

approximation algorithmssparsest cutsminimum normalized cutspairwise data clustering

Mathematics Subject Classification ID

Graph theory (including graph drawing) in computer science (68R10) Graph algorithms (graph-theoretic aspects) (05C85) Approximation algorithms (68W25)

Related Items

Mean isoperimetry with control on outliers: exact and approximation algorithms ⋮ On the complexity of isoperimetric problems on trees ⋮ An approximate distribution for the normalized cut

Cites Work

• Fast approximation algorithms for multicommodity flow problems
• Multicommodity max-flow min-cut theorems and their use in designing approximation algorithms
• The maximum concurrent flow problem
• Fast Approximate Graph Partitioning Algorithms
• Approximate Max-Flow Min-(Multi)Cut Theorems and Their Applications
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