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. 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.

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Package
Spectrum
Title
Fast Adaptive Spectral Clustering for Single and Multi-View Data
Version
0.5
Author
Christopher R John, David Watson
Maintainer
Christopher R John
Description
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.
Depends
R (>= 3.5.0)
License
AGPL-3
Encoding
UTF-8
LazyData
true
Imports
ggplot2, Rtsne, ClusterR, umap, Rfast, RColorBrewer, diptest
Suggests
knitr
VignetteBuilder
knitr
RoxygenNote
6.1.1
NeedsCompilation
no
Packaged
2019-04-08 11:54:57 UTC; christopher
Repository
CRAN
Date/Publication
2019-04-08 12:53:08 UTC

install.packages('Spectrum')

0.5

15 days ago

Christopher R John

AGPL-3

Depends on

R (>= 3.5.0)

Imports

ggplot2, Rtsne, ClusterR, umap, Rfast, RColorBrewer, diptest

Suggests

knitr

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