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

# Fast Adaptive Spectral Clustering for Single and Multi-View Data

## Documentation for package ‘Spectrum’ version 1.1

- DESCRIPTION file
- User guides, package vignettes and other documentation

## Help Pages

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