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Spectrum (version 0.2)

Versatile Ultra-Fast Spectral Clustering for Single and Multi-View Data

Description

A versatile ultra-fast spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in dense regions in the graph. For integrating multi-view data and reducing noise we use a recently developed tensor product graph data integration and diffusion system. 'Spectrum' contains two techniques for finding the number of clusters (K); the classical eigengap method and a novel multimodality gap procedure. The multimodality gap analyses the distribution of the eigenvectors of the graph Laplacian to decide K and tune the kernel. 'Spectrum' is suited for clustering a wide range of complex data.

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Version

Install

install.packages('Spectrum')

Monthly Downloads

322

Version

0.2

License

AGPL-3

Maintainer

Christopher John

Last Published

February 11th, 2019

Functions in Spectrum (0.2)

blobs

8 blob like structures
brain

A brain cancer dataset
pca

pca: A pca function
rbfkernel_b

rbfkernel_b: fast self-tuning kernel
CNN_kernel_mine_b

CNN_kernel_mine_b: fast adaptive density aware kernel
Spectrum

Spectrum: Versatile ultra-fast spectral clustering for single and multi-view data
spirals

Two spirals wrapped around one another
tsne

tsne: A tsne function for similarity matrices or ordinary data
circles

Three concentric circles
kernel_pca

kernel_pca: A kernel pca function
umap

umap: A umap function for similarity matrices or ordinary data