library('mlbench')
library('manipulate')
set.seed(42)
dat <- mlbench.spirals(500,1,0.05)

## spirals data is a two-class classification 
## problem with two features
plot(dat$x, xlab=expression(x[1]),
     ylab=expression(x[2]), col=dat$classes,
     main="Truth")

## usign k-means
kmn <- kmeans(dat$x, 2, nstart = 5)
plot(dat$x, xlab=expression(x[1]),
     ylab=expression(x[2]), col=kmn$cluster,
     main="K-means")

## using agglomeration w/complete linkage
hcl <- cutree(hclust(dist(dat$x), method='complete'), k=2)
plot(dat$x, xlab=expression(x[1]),
     ylab=expression(x[2]), col=hcl,
     main="H-clust Complete Linkage")

## using agglomeration w/average linkage
hcl <- cutree(hclust(dist(dat$x), method='average'), k=2)
plot(dat$x, xlab=expression(x[1]),
     ylab=expression(x[2]), col=hcl,
     main="H-clust Average Linkage")

## using agglomeration w/centroid linkage
hcl <- cutree(hclust(dist(dat$x), method='centroid'), k=2)
plot(dat$x, xlab=expression(x[1]),
     ylab=expression(x[2]), col=hcl,
     main="H-clust Centroid Linkage")


## using agglomeration w/single linkage
hcl <- cutree(hclust(dist(dat$x), method='single'), k=2)
plot(dat$x, xlab=expression(x[1]),
     ylab=expression(x[2]), col=hcl,
     main="H-clust Single Linkage")