Silh: Probabilistic silhouette plot

In FPDclustering: PD-Clustering and Factor PD-Clustering

Probabilistic silhouette plot

Description

Graphical tool to evaluate the clustering partition.

Usage

Silh(p)

Arguments

p	A matrix of probabilities such that rows correspond to observations and columns correspond to clusters.

Details

The probabilistic silhouettes are an adaptation of the ones proposed by Menardi(2011) according to the following formula:

dbs_i = (log(p_{im_k}/p_{im_1}))/max_i |log(p_{im_k}/p_{im_1})|

,

where m_k is such that x_i belongs to cluster k and m_1 is such that p_{im_1} is maximum for m different fromm_k.

Value

Probabilistic silhouette plot

Author(s)

Cristina Tortora

References

Menardi G. Density-based Silhouette diagnostics for clustering methods.Statistics and Computing, 21, 295-308, 2011.

Examples

## Not run: 
# Asymmetric data set silhouette example (with shape=3).
data('asymmetric3')
x<-asymmetric3[,-1]
fpdas3=FPDC(x,4,3,3)
Silh(fpdas3$probability)

## End(Not run)

## Not run: 
# Asymmetric data set shiluette example (with shape=20).
data('asymmetric20')
x<-asymmetric20[,-1]
fpdas20=FPDC(x,4,3,3)
Silh(fpdas20$probability)

## End(Not run)

## Not run: 
# Shiluette example with outliers.
data('outliers')
x<-outliers[,-1]
fpdout=FPDC(x,4,4,3)
Silh(fpdout$probability)

## End(Not run)

FPDclustering documentation built on Aug. 31, 2022, 5:09 p.m.