ClusterPlotMDS: Plot Clustering using Dimensionality Reduction by MDS

Description Usage Arguments Details Value Note Author(s) References See Also Examples

View source: R/ClusterPlotMDS.R

Description

This function uses a projection method to perform dimensionality reduction (DR) on order to visualize the data as 3D data points colored by a clustering.

Usage

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ClusterPlotMDS(DataOrDistances, Cls, main = "Clustering",

DistanceMethod = "euclidean", OutputDimension = 3,

PointSize=1,Plotter3D="rgl",Colorsequence, ...)

Arguments

DataOrDistances

Either nonsymmetric [1:n,1:d] datamatrix of n cases and d features or symmetric [1:n,1:n] distance matrix

Cls

1:n numerical vector of numbers defining the classification as the main output of the clustering algorithm for the n cases of data. It has k unique numbers representing the arbitrary labels of the clustering.

main

String, title of plot

DistanceMethod

Method to compute distances, default "euclidean"

OutputDimension

Either two or three depending on user choice

PointSize

Scalar defining the size of points

Plotter3D

In case of 3 dimensions, choose either "plotly" or "rgl",

Colorsequence

[1:k] character vector of colors, per default the colorsquence defined in the DataVisualizations is used

...

Please see Plot3D in DataVisualizations

Details

If dataset has more than 3 dimesions, mds is performed as defined in the smacof [De Leeuw/Mair, 2011]. If smacof package is not installed, classical metric MDS (see Def. in [Thrun, 2018]) is performed. In both cases, the first OutputDimension are visualized. Points are colored by the labels (Cls).

In the special case that the dataset has not more than 3 dimensions, all dimensions are visualized and no DR is performed.

Value

The rgl or plotly plot handler depending on Plotter3D

Note

If DataVisualizations is not installed a 2D plot using native plot function is shown.

If MASS is not installed, classicial metric MDS is used, see [Thrun, 2018] for definition.

Author(s)

Michael Thrun

References

[De Leeuw/Mair, 2011] De Leeuw, J., & Mair, P.: Multidimensional scaling using majorization: SMACOF in R, Journal of statistical Software, Vol. 31(3), pp. 1-30. 2011.

[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, ISBN: 978-3-658-20539-3, Heidelberg, 2018.

See Also

Plot3D

Examples

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FCPS documentation built on July 8, 2021, 1:06 a.m.