# 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 No Results!