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. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' analyses eigenvector variance or distribution to determine the number of clusters. The method is well suited for a wide range of data, including both Gaussian and non-Gaussian structures.
| Version: | 0.5 |
| Depends: | R (≥ 3.5.0) |
| Imports: | ggplot2, Rtsne, ClusterR, umap, Rfast, RColorBrewer, diptest |
| Suggests: | knitr |
| Published: | 2019-04-08 |
| 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_0.5.tar.gz |
| Windows binaries: | r-devel: Spectrum_0.5.zip, r-release: Spectrum_0.5.zip, r-oldrel: Spectrum_0.5.zip |
| OS X binaries: | r-release: Spectrum_0.5.tgz, r-oldrel: Spectrum_0.5.tgz |
| Old sources: | Spectrum archive |
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