## Hierarchical clustering (9/05/24) 
?agnes
?eurodist

data(eurodist)
as.matrix(eurodist)[1:5, 1:5]

library(cluster)
#single linkage
agnes.single<-agnes(eurodist, method="single")
# agglomerative coefficient
agnes.single$ac
pltree(agnes.single, cex=0.8, hang = -1, main = "agnes (single)", xlab="", sub ="")

# Oppure
?hclust
hc<-hclust(eurodist, method="single")
plot(hc, hang=-1)

#complete linkage
agnes.comp<-agnes(eurodist, method="complete")
agnes.comp$ac
pltree(agnes.comp, cex = 0.8, hang = -1, main = "agnes (complete)", xlab="", sub ="")

#average linkage
agnes.ave<-agnes(eurodist, method="average")
agnes.ave$ac
pltree(agnes.ave, hang=-1, cex = 0.7, main="")
abline(h=1000)
rect.hclust(agnes.ave, k = 4, border = 3:7)
hc<-cutree(agnes.ave, 4)

cnames<-row.names(as.matrix(eurodist))
cnames[hc==1]
cnames[hc==2]
cnames[hc==3]
cnames[hc==4]

# Ward's method
agnes.Ward<-agnes(eurodist, method="ward")
agnes.Ward$ac
pltree(agnes.Ward, cex = 0.8, hang = -1, main = "agnes (Ward)", xlab="", sub ="")