# ClusterPlotMDS: Plot Clustering using Dimensionality Reduction by MDS In FCPS: Fundamental Clustering Problems Suite

 ClusterPlotMDS R Documentation

## Plot Clustering using Dimensionality Reduction by MDS

### 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

```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.

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.

`Plot3D`

### Examples

```data(Hepta)
ClusterPlotMDS(Hepta\$Data,Hepta\$Cls)

data(Leukemia)
ClusterPlotMDS(Leukemia\$DistanceMatrix,Leukemia\$Cls)

```

FCPS documentation built on May 20, 2022, 5:06 p.m.