R: Silhouette plot of classified data.

Silhouetteplot {DataVisualizations} | R Documentation

## Silhouette plot of classified data.

### Description

Silhouette plot of cluster silhouettes for the n-by-d data matrix Data or distance matrix where the clusters are defined in the vector Cls.

### Usage

    Silhouetteplot(DataOrDistances, Cls, method='euclidean',

    PlotIt=TRUE,...)

### Arguments

`DataOrDistances` | [1:n,1:d] data cases in rows, variables in columns, if not symmetric or [1:n,1:n] distance matrix, if symmetric
`Cls` | numeric vector, [1:n,1] classified data
`method` | Optional if Datamatrix is used, one of "euclidean", "maximum", "manhattan", "canberra", "binary" or "minkowski". Any unambiguous substring can be given, see `dist`
`PlotIt` | Optional, Default:TRUE, FALSE to supress the plot
`...` | If `PlotIt=TRUE`: Further arguements to `barplot`

### Details

"The Silhouette plot is a common unsupervised index for visual evaluation of a clustering [L. R. Kaufman/Rousseeuw, 2005] [introduced in [Rousseeuw, 1987]]. A reasonable clustering is characterized by a silhouette width of greater than 0.5, and an average width below 0.2 should be interpreted as indicating a lack of any substantial cluster structure [Everitt et al., 2001, p. 105]. However, it is evident that silhouette scores assume clusters that are spherical or Gaussian in shape [Herrmann, 2011, pp. 91-92]" [Thrun, 2018, p. 29].

### Value

`silh` | Silhouette values in a N-by-1 vector

### Author(s)

Onno Hansen-Goos, Michael Thrun

### References

[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.

[Rousseeuw, 1987] Rousseeuw, Peter J.: Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis, Computational and Applied Mathematics, 20, p.53-65, 1987.

### Examples

    data("Lsun3D")
    Cls=Lsun3D$Cls
    Data=Lsun3D$Data
    #clear cluster structure
    plot(Data[,1:2],col=Cls)
    #However, the silhouette plot does not indicate a very good clustering in cluster 1 and 2
    Silhouetteplot(Data,Cls = Cls,main='Silhouetteplot')

[Package DataVisualizations version 1.2.3 Index]