Fast Adaptive Spectral Clustering for Single and Multi-View Data

Documentation for package ‘Spectrum’ version 1.1

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