Spectrum v0.2


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

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.

Functions in Spectrum

Name Description
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
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License AGPL-3
Encoding UTF-8
LazyData true
VignetteBuilder knitr
RoxygenNote 6.1.1
NeedsCompilation no
Packaged 2019-02-11 07:19:51 UTC; christopher
Repository CRAN
Date/Publication 2019-02-11 08:43:19 UTC

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