plot.pkbc: Plotting method for Poisson kernel-based clustering

In QuadratiK: Collection of Methods Constructed using Kernel-Based Quadratic Distances

Plotting method for Poisson kernel-based clustering

Description

Plots for a pkbc object.

Usage

## S4 method for signature 'pkbc,ANY'
plot(x, k = NULL, true_label = NULL, pca_res = FALSE, ...)

Arguments

x	Object of class pkbc
k	number of considered clusters. If it is not provided the scatter plot is displayed for each value of number of clusters present in the x object
true_label	factor or vector of true membership to clusters (if available). It must have the same length of final memberships.
pca_res	Logical. If TRUE the results from PCALocantore are also reported (when dimension is greater than 3).
...	Additional arguments that can be passed to the plot function

Details

• scatterplot: If dimension is equal to 2 or 3, points are displayed on the circle and sphere, respectively. If dimension if greater than 3, the spherical Principal Component procedure proposed by Locantore et al. (1999), is applied for dimensionality reduction and the first three principal components are normalized and displayed on the sphere. For d > 3, the complete results from the PcaLocantore function (package rrcov) are returned if pca_res=TRUE.

• elbow plot: the within cluster sum of squares (wcss) is computed using the Euclidean distance (left) and the cosine similarity (right).

Value

The scatter-plot(s) and the elbow plot.

Note

The elbow plot is commonly used as a graphical method for choosing the appropriate number of clusters. Specifically, plotting the wcss versus the number of clusters, the suggested number of clusters correspond to the point in which the plotted line has the greatest change in slope, showing an elbow.

References

Locantore, N., Marron, J.S., Simpson, D.G. et al. (1999) "Robust principal component analysis for functional data." Test 8, 1–73. https://doi.org/10.1007/BF02595862

See Also

pkbc() for the clustering algorithm
pkbc for the class object definition.

Examples

dat<-matrix(rnorm(300),ncol=3)
pkbc_res<- pkbc(dat, 3)
plot(pkbc_res, 3)

QuadratiK documentation built on Oct. 29, 2024, 5:08 p.m.