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How the result of graph clustering methods depends on the construction of the graph

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Publication:5408477
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DOI10.1051/ps/2012001zbMath1284.62382OpenAlexW2133896597MaRDI QIDQ5408477

Matthias Hein, Ulrike von Luxburg, Markus Maier

Publication date: 10 April 2014

Published in: ESAIM: Probability and Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1051/ps/2012001

zbMATH Keywords

random geometric graphsgraph cuts


Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Random graphs (graph-theoretic aspects) (05C80) Probability in computer science (algorithm analysis, random structures, phase transitions, etc.) (68Q87)


Related Items

Adaptive edge weighting for graph-based learning algorithms, Randomized near-neighbor graphs, giant components and applications in data science, Theoretical Analysis of Active Contours on Graphs, Continuum limit of total variation on point clouds



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