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###  489/589 ML Ch10 Lab 3 - Clustering Analysis
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###--------------- NCI60 Data --------------------
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###-------------------------------------------------
###--- 1. Preliminary


library (ISLR)
nci.labs=NCI60$labs
nci.data=NCI60$data

dim(nci.data)


nci.labs[1:4]
#  [1] "CNS " "CNS" "CNS" "RENAL"

table(nci.labs)


pr.out =prcomp (nci.data, scale=TRUE)


Cols <- function(vec){
    cols=rainbow (length (unique (vec )))
    return (cols[as.numeric (as.factor (vec))])
}


par(mfrow =c(1,2))
plot(pr.out$x [,1:2], col =Cols(nci .labs), pch =19, xlab ="Z1",ylab="Z2")
plot(pr.out$x[,c(1,3) ], col =Cols(nci.labs), pch =19, xlab ="Z1",ylab="Z3")


summary (pr.out)


plot(pr.out)


pve =100* pr.out$sdev ^2/ sum(pr.out$sdev ^2)
par(mfrow=c(1,2))
plot(pve, type="o", ylab="PVE ", xlab=" Principal Component ", col =" blue")
plot(cumsum(pve), type="o", ylab =" Cumulative PVE", xlab=" Principal Component ", col =" brown3 ")


sd.data=scale(nci.data)




par(mfrow =c(1,3))
data.dist=dist(sd.data)
plot( hclust(data.dist), labels =nci.labs , main="Complete Linkage", xlab="", sub="", ylab="")
plot( hclust(data.dist , method ="average"), labels=nci.labs, main="Average Linkage", xlab="", sub="", ylab="")
plot( hclust(data.dist , method ="single"),  labels=nci.labs, main="Single Linkage",  xlab="", sub="", ylab="")


hc.out =hclust(dist(sd.data))
hc.clusters =cutree(hc.out,4)
table(hc.clusters,nci.labs)



par(mfrow =c(1,1))
plot(hc.out, labels=nci.labs)
abline (h=139, col="red")

hc.out



set.seed (2)
km.out=kmeans(sd.data, 4, nstart =20)
km.clusters=km. out$cluster
table(km.clusters, hc.clusters)


hc.out=hclust (dist(pr.out$x [ ,1:5]) )
plot(hc.out, labels=nci.labs, main=" Hier. Clust. on First Five Score Vectors ")
table(cutree(hc.out ,4) , nci.labs)