# Aufgabe 1

data(eurodist)
e<-eurodist
e[18]<-1420


hc1<-hclust(e,method="ave")
plot(hc1)
get(options()$device)() # open new dev - no matter what platform

hc2<-hclust(e,method="war")
plot(hc2)

le<-cutree(hc1,4)
le2<-cmdscale(e)

plot(le2,col=le)
text(le2,names(le),adj=c(0,1))

# Aufgabe 2

cr<-read.table("~/Datasets/crabs.txt",T)
crv<-cr[4:8]

cd<-dist(crv)
m<-c("wa","si","co","av") # methods
c1<-lapply(m,function(x) hclust(cd, method=x))

crp<-predict(princomp(crv))
cpd<-dist(crp)
c2<-lapply(m,function(x) hclust(cpd, method=x))

cl<-c(c1,c2)
for (i in cl) plot(crv[1:2],col=cutree(i,4))

# Aufgabe 4

mc<-Mclust(crv,1,12)
plot(mc,crv)

mc3<-Mclust(crv,3,3)
plot(mc,crv)

jf<-as.factor(paste(cr$sp,cr$sex,sep=''))
table(mc$classification,jf)
table(mc3$classification,jf)

# Aufgabe 5

mc2<-Mclust(crv,2,2)
cr1<-subset(cr,mc2$cl==1)
cr2<-subset(cr,mc2$cl==2)
mc2.1<-Mclust(cr1[4:8],2,2)
mc2.2<-Mclust(cr2[4:8],2,2)
ncr<-rbind(cr1,cr2)
cl<-c(mc2.1$cl,(mc2.2$cl+2))
plot(ncr[4:5],col=cl,pch=(sex-1)*2+sp)
njf<-as.factor(paste(ncr$sp,ncr$sex,sep=''))
table(njf, cl)

# ohne BD:
mc2<-Mclust(cr[4:7],2,2)
cr1<-subset(cr,mc2$cl==1)
cr2<-subset(cr,mc2$cl==2)
mc2.1<-Mclust(cr1[4:7],2,2)
mc2.2<-Mclust(cr2[4:7],2,2)
ncr<-rbind(cr1,cr2)
cl<-c(mc2.1$cl,(mc2.2$cl+2))
plot(ncr[4:5],col=cl,pch=(sex-1)*2+sp)
njf<-as.factor(paste(ncr$sp,ncr$sex,sep=''))
table(njf, cl)