Graph clustering by congruency approximation

The authors consider the general problem of graph clustering. Graph clustering manipulates the graph-based data structure and the entries of the solution vectors are only allowed to take non-negative discrete values. Finding the optimal solution is NP-hard, so relaxations are usually considered. Spe...

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Veröffentlicht in:	IET computer vision 2015-12, Vol.9 (6), p.841-849
Hauptverfasser:	Ren, Weiya, Li, Guohui, Tu, Dan
Format:	Artikel
Sprache:	eng
Schlagworte:	Approximation CAC Clustering computational complexity congruency approximation congruent approximate graph clustering graph theory graph-based data structure Graphs ideal discrete solution Mathematical analysis Mathematical models matrix decomposition nonnegative discrete values nonnegative matrix factorisation NP-hard optimal solution Optimization Orthogonality pattern clustering real image benchmark datasets solution vectors Spectra spectral clustering
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