SpectralClustering {FCPS}

Spectral Clustering

Description

Clusters the Data into "ClusterNo" different clusters using the Spectral Clustering method

Usage

SpectralClustering(Data, ClusterNo,PlotIt=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.

ClusterNo

A number k which defines k different clusters to be built by the algorithm.

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

...

Further arguments to be set for the clustering algorithm, if not set, default arguments are used. e.g.:

kernel : Kernelmethod, possible options: rbfdot Radial Basis kernel function "Gaussian" polydot Polynomial kernel function vanilladot Linear kernel function tanhdot Hyperbolic tangent kernel function laplacedot Laplacian kernel function besseldot Bessel kernel function anovadot ANOVA RBF kernel function splinedot Spline kernel stringdot String kernel

kpar : Kernelparameter: a character string or the list of hyper-parameters (kernel parameters). The default character string "automatic" uses a heuristic to determine a suitable value for the width parameter of the RBF kernel. "local" (local scaling) uses a more advanced heuristic and sets a width parameter for every point in the data set. A list can also be used containing the parameters to be used with the kernel function.

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

[Ng et al., 2002] Ng, A. Y., Jordan, M. I., & Weiss, Y.: On spectral clustering: Analysis and an algorithm, Advances in neural information processing systems, Vol. 2, pp. 849-856. 2002.

Examples

data('Hepta')
out=SpectralClustering(Hepta$Data,ClusterNo=7,PlotIt=FALSE)