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Accelerated sparse Kernel Spectral Clustering for large scale data clustering problems
This is altered by the modifications reported in this brief that drastically improve the computational characteristics.
Kernel Spectral Clustering and applications
This is a major advantage compared to classical spectral clustering where the determination of the clustering parameters is unclear and relies on heuristics.
Clustering evolving data using kernel-based methods
This paves the way for the development of a novel clustering algorithm for the analysis of evolving networks called kernel spectral clustering with memory effect (MKSC), where the temporal smoothness between clustering results in successive time steps is incorporated at the level of the primal optimization problem, by properly modifying the KSC formulation.
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