Fast Adaptive Spectral Clustering for Single and Multi-View Data

A self-tuning spectral clustering method for single or multi-view data. 'Spectrum' uses a new type of adaptive density aware kernel that strengthens connections in the graph based on common nearest neighbours. 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. The method is sufficiently flexible so that a wide range of Gaussian and non-Gaussian structures can be clustered with automatic selection of K.


Reference manual

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1.1 by Christopher R John, a year ago

Browse source code at

Authors: Christopher R John , David Watson

Documentation:   PDF Manual  

Task views: Cluster Analysis & Finite Mixture Models

AGPL-3 license

Imports ggplot2, ClusterR, Rfast, diptest

Suggests knitr

Suggested by FCPS.

See at CRAN