Spectrum: Fast Adaptive Spectral Clustering for Single and Multi-View Data

A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in the graph. For integrating multi-view data and reducing noise a tensor product graph data integration and diffusion procedure is used. 'Spectrum' analyses eigenvector variance or distribution to determine the number of clusters. 'Spectrum' is well suited for a wide range of data, including both Gaussian and non-Gaussian structures.

Version: 0.4
Depends: R (≥ 3.5.0)
Imports: ggplot2, Rtsne, ClusterR, umap, Rfast, RColorBrewer, diptest
Suggests: knitr
Published: 2019-03-15
Author: Christopher R John, David Watson
Maintainer: Christopher R John <chris.r.john86 at gmail.com>
License: AGPL-3
NeedsCompilation: no
CRAN checks: Spectrum results


Reference manual: Spectrum.pdf
Vignettes: Spectrum
Package source: Spectrum_0.4.tar.gz
Windows binaries: r-devel: Spectrum_0.4.zip, r-release: Spectrum_0.4.zip, r-oldrel: not available
OS X binaries: r-release: Spectrum_0.4.tgz, r-oldrel: not available
Old sources: Spectrum archive


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