Spectrum {FCPS} | R Documentation |
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 |
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 |
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
Examples
data('Hepta')
out=Spectrum(Hepta$Data,PlotIt=FALSE)
out=Spectrum(Hepta$Data,PlotIt=TRUE)