DataVisualizations (version 1.1.12)

Heatmap: Heatmap for Clustering

Description

Heatmap of Distances of Data sorted by Cls

Usage

Heatmap(DataOrDistances,Cls,method='euclidean',LowLim=0,HiLim)

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 Data[1:n,1:d] see dist for distance method

LowLim

Optional: limits for the color axis

HiLim

Optional: limits for the color axis

Value

object of ggplot2

Details

Clustering algorithms provide a Classifcation of data, where the labels are defined as a numeric vector Cls

Then, a typical cluster-respectively group structure is displayed by the HeatMap function. At the margin of the heatmap a dendrogram can be shown, if hierarchical cluster algorithms are used. Here the dendrogram has to be shown separately and only the heatmap itself is displayed [Wilkinson,2009].

More details in [Thrun, 2018, p. 29]

References

[Wilkinson,2009] Wilkinson, L., & Friendly, M.: The history of the cluster heat map, The American Statistician, Vol. 63(2), pp. 179-184. 2009.

[Thrun, 2018] Thrun, M. C.: Projection Based Clustering through Self-Organization and Swarm Intelligence, doctoral dissertation 2017, Springer, Heidelberg, ISBN: 978-3-658-20539-3, https://doi.org/10.1007/978-3-658-20540-9, 2018.

See Also

Pixelmatrix

Examples

# NOT RUN {
data("Lsun3D")
Cls=Lsun3D$Cls
Data=Lsun3D$Data
# }
# NOT RUN {
Heatmap(as.matrix(dist(Data)),Cls = Cls)
# }