Packaged: 2019-02-11 07:19:51 UTC; christopher

Author: Christopher R John, David Watson
Packaged: 2019-02-15 14:39:19 UTC; christopher

Title: Fast Adaptive Spectral Clustering for Single and Multi-View Data
Description: A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in the graph. For integrating multi-view data and reducing noise a tensor product graph data integration and diffusion procedure is used. 'Spectrum' analyses eigenvector variance or distribution to determine the number of clusters. 'Spectrum' is well suited for a wide range of data, including both Gaussian and non-Gaussian structures.
Packaged: 2019-03-15 09:57:26 UTC; christopher

Description: A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in the graph. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' analyses eigenvector variance or distribution to determine the number of clusters. The method is well suited for a wide range of data, including both Gaussian and non-Gaussian structures.
Packaged: 2019-04-08 11:54:57 UTC; christopher

Description: A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens local connections in the graph. It uses a tensor product graph data integration and diffusion procedure to integrate different data sources and reduce noise. 'Spectrum' uses either the eigengap or multimodality gap heuristics to determine the number of clusters. 'Spectrum' was developed for clustering complex single and multi-omic data. However, the method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.
Packaged: 2019-05-28 07:42:57 UTC; christopher