Spectrum: Fast Adaptive Spectral Clustering [John et al, 2020]

In FCPS: Fundamental Clustering Problems Suite

Fast Adaptive Spectral Clustering [John et al, 2020]

Description

Spectrum is a self-tuning spectral clustering method for single or multi-view data. In this wrapper restricted to the standard use in other clustering algorithms.

Usage

Spectrum(Data, Type = 2, ClusterNo = NULL, 

PlotIt = FALSE, Silent = TRUE,PlotResults = FALSE, ...)

Arguments

Data	1:n,1:d] matrix of dataset to be clustered. It consists of n cases of d-dimensional data points. Every case has d attributes, variables or features.
Type	Type=1: default eigengap method (Gaussian clusters) Type=2: multimodality gap method (Gaussian/ non-Gaussian clusters) Type=3: Allows to setClusterNo
ClusterNo	Optional, A number k which defines k different clusters to be built by the algorithm. For default ClusterNo=NULL please see details.
PlotIt	Default: FALSE, If TRUE plots the first three dimensions of the dataset with colored three-dimensional data points defined by the clustering stored in Cls
Silent	Silent progress of algorithm=TRUE
PlotResults	Plots result of spectrum with plot function
...	Method: numerical value: 1 = default eigengap method (Gaussian clusters), 2 = multimodality gap method (Gaussian/ non-Gaussian clusters), 3 = no automatic method (see fixk param) Other parameters defined in Spectrum packages

Details

Spectrum is a partitioning algorithm and either uses the eigengap or multimodality gap heuristics to determine the number of clusters, please see Spectrum package for details

Value

List of

Cls	[1:n] numerical vector with n numbers defining the classification as the main output of the clustering algorithm. It has k unique numbers representing the arbitrary labels of the clustering.
Object	Object defined by clustering algorithm as the other output of this algorithm

Author(s)

Michael Thrun

References

[John et al, 2020] John, C. R., Watson, D., Barnes, M. R., Pitzalis, C., & Lewis, M. J.: Spectrum: Fast density-aware spectral clustering for single and multi-omic data. Bioinformatics, Vol. 36(4), pp. 1159-1166, 2020.

See Also

Spectrum

Examples

data('Hepta')
out=Spectrum(Hepta$Data,PlotIt=FALSE)

out=Spectrum(Hepta$Data,PlotIt=TRUE)

FCPS documentation built on Oct. 19, 2023, 5:06 p.m.