경희대학교 BK21

## 가장 단순한 k-NN 클러스터링 방법
newiris <- iris
newiris$Species <- NULL
(kc <- kmeans(newiris, 3))
table(iris$Species, kc$cluster)
plot(newiris[c("Sepal.Length", "Sepal.Width")], col=kc$cluster)
points(kc$centers[,c("Sepal.Length", "Sepal.Width")], col=1:3, pch=8, cex=2)

 

## 좀 더 전문적인 방법
install.packages("cluster")
library(cluster)
data(votes.repub)
agn1 <- agnes(votes.repub, metric = "manhattan", stand = TRUE)
agn1
plot(agn1)

 

op <- par(mfrow=c(2,2))
agn2 <- agnes(daisy(votes.repub), diss = TRUE, method = "complete")
plot(agn2)
## alpha = 0.625 ==> beta = -1/4 is "recommended" by some
agnS <- agnes(votes.repub, method = "flexible", par.meth = 0.625)
plot(agnS)
par(op)

 

## "show" equivalence of three "flexible" special cases
d.vr <- daisy(votes.repub)
a.wgt <- agnes(d.vr, method = "weighted")
a.sing <- agnes(d.vr, method = "single")
a.comp <- agnes(d.vr, method = "complete")
iC <- -(6:7) # not using 'call' and 'method' for comparisons
stopifnot(
all.equal(a.wgt [iC], agnes(d.vr, method="flexible", par.method = 0.5)[iC]) ,
all.equal(a.sing[iC], agnes(d.vr, method="flex", par.method= c(.5,.5,0, -.5))[iC]),
all.equal(a.comp[iC], agnes(d.vr, method="flex", par.method= c(.5,.5,0, +.5))[iC]))

 

## Exploring the dendrogram structure
(d2 <- as.dendrogram(agn2)) # two main branches
d2[[1]] # the first branch
d2[[2]] # the 2nd one { 8 + 42 = 50 }
d2[[1]][[1]]# first sub-branch of branch 1 .. and shorter form
identical(d2[[c(1,1)]], d2[[1]][[1]])

 

## a "textual picture" of the dendrogram :
agnes.object 7
str(d2)
data(agriculture)

 

## Plot similar to Figure 7 in ref
## Not run: plot(agnes(agriculture), ask = TRUE)
data(animals)
aa.a <- agnes(animals) # default method = "average"
aa.ga <- agnes(animals, method = "gaverage")
op <- par(mfcol=1:2, mgp=c(1.5, 0.6, 0), mar=c(.1+ c(4,3,2,1)),
cex.main=0.8)
plot(aa.a, which.plot = 2)
plot(aa.ga, which.plot = 2)
par(op)

 

## Show how "gaverage" is a "generalized average":
aa.ga.0 <- agnes(animals, method = "gaverage", par.method = 0)
stopifnot(all.equal(aa.ga.0[iC], aa.a[iC]))