gauss.kmedoids: kmedoids

In kyoustat/T4Gauss: Tools and Methods for Gaussian Distribution

Description

k-Medoids is a generally applicable clustering algorithm as long as we have concept of dissimilarity. We adopt pam algorithm by cluster package. See pam for more details.

Usage

gauss.kmedoids(glist, k = 2, type = c("wass2"))

Arguments

glist	list of objects, a S3 object of riemdata class. See riemfactory for more details.
k	the number of clusters.
type	type of distance metric to be used.

Examples

## generate three-cluster data with univariate Gaussians
mylist = list()
for (i in 1:10){
   mylist[[i]] = wrapgauss1d(mean=-2-runif(1), sd=runif(1))
}
for (i in 11:20){
   mylist[[i]] = wrapgauss1d(mean=0, sd=runif(1))
}
for (i in 21:30){
   mylist[[i]] = wrapgauss1d(mean=2+runif(1), sd=runif(1))
}

## apply clustering with different k values
cl2 <- gauss.kmedoids(mylist, k=2)$cluster
cl3 <- gauss.kmedoids(mylist, k=3)$cluster
cl4 <- gauss.kmedoids(mylist, k=4)$cluster

## compute 2-dimensional embedding for visualization
mds2d <- gauss.mds(mylist, ndim=2)$embed
mdsx <- as.vector(mds2d[,1])
mdsy <- as.vector(mds2d[,2])

## visualize
opar = par(mfrow=c(1,3), pty="s")
plot(mdsx, mdsy, pch=19, col=cl2, main="k=2 medoids")
plot(mdsx, mdsy, pch=19, col=cl3, main="k=3 medoids")
plot(mdsx, mdsy, pch=19, col=cl4, main="k=4 medoids")
par(opar)

kyoustat/T4Gauss documentation built on April 9, 2020, 10:47 a.m.