Package: Spectrum 1.1

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 connections in the graph based on common nearest neighbours. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.

Authors:Christopher R John, David Watson

Spectrum_1.1.tar.gz
Spectrum_1.1.zip(r-4.7)Spectrum_1.1.zip(r-4.6)Spectrum_1.1.zip(r-4.5)
Spectrum_1.1.tgz(r-4.6-any)Spectrum_1.1.tgz(r-4.5-any)
Spectrum_1.1.tar.gz(r-4.7-any)Spectrum_1.1.tar.gz(r-4.6-any)
Spectrum_1.1.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION
card.svg |card.png
Spectrum/json (API)

# Install 'Spectrum' in R:

install.packages('Spectrum', repos = c('https://crj32.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/crj32/spectrum/issues

Datasets:

blobs - 8 blob like structures
brain - A brain cancer dataset
circles - Three concentric circles
missl - A list of the blob data as similarity matrices with a missing entry in one
misslfilled - A list of the blob data as similarity matrices with a missing entry in one filled with NAs
spirals - Two spirals wrapped around one another

On CRAN:

clustering spectral-clustering

6.56 score 7 stars 2 packages 86 scripts 496 downloads 450 mentions 12 exports 25 dependencies

Last updated from:92c21c8162. Checks:7 WARNING, 2 OK. Indexed: yes.

Target	Result	Time
linux-devel-x86_64	WARNING	159
source / vignettes	OK	221
linux-release-x86_64	WARNING	125
macos-release-arm64	WARNING	115
macos-oldrel-arm64	WARNING	94
windows-devel	WARNING	117
windows-release	WARNING	101
windows-oldrel	WARNING	83
wasm-release	OK	108

Exports:cluster_similarity CNN_kernel estimate_k harmonise_ids integrate_similarity_matrices kernel_pca mean_imputation ng_kernel pca rbfkernel_b sigma_finder Spectrum

Dependencies:cli ClusterR cpp11 diptest farver ggplot2 glue gmp gtable isoband labeling lifecycle R6 RColorBrewer Rcpp RcppArmadillo RcppParallel Rfast rlang S7 scales vctrs viridisLite withr zigg

Spectrum

Rendered fromSpectrum_vignette.Rmdusingknitr::rmarkdown

Last update: 2020-02-12
Started: 2020-02-12

Help page	Topics
8 blob like structures	blobs
A brain cancer dataset	brain
Three concentric circles	circles
cluster_similarity: cluster a similarity matrix using the Ng method	cluster_similarity
CNN_kernel: fast adaptive density-aware kernel	CNN_kernel
estimate_k: estimate K using the eigengap or multimodality gap heuristics	estimate_k
harmonise_ids: works on a list of similarity matrices to add entries of NA where there are missing observations between views	harmonise_ids
integrate_similarity_matrices: integrate similarity matrices using a tensor product graph linear combination and diffusion technique	integrate_similarity_matrices
kernel_pca: A kernel pca function	kernel_pca
mean_imputation: mean imputation function for multi-view spectral clustering with missing data	mean_imputation
A list of the blob data as similarity matrices with a missing entry in one	missl
A list of the blob data as similarity matrices with a missing entry in one filled with NAs	misslfilled
ng_kernel: Kernel from the Ng spectral clustering algorithm	ng_kernel
pca: A pca function	pca
rbfkernel_b: fast self-tuning kernel	rbfkernel_b
sigma_finder: heuristic to find sigma for the Ng kernel	sigma_finder
Spectrum: Fast Adaptive Spectral Clustering for Single and Multi-view Data	Spectrum
Two spirals wrapped around one another	spirals

Package: Spectrum 1.1

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

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Usage by other packages (reverse dependencies)