clust <- read.table("W:/R/task9data.txt",header=F, sep=",")

# Give row names and column names
cnames <- c("country","wine","heart")
colnames(clust) <- cnames
rownames(clust) <-clust$country


# Hierarchical Clustering
# Create distance matrix
d <- dist(clust, method = "euclidean") 
fit <- hclust(d)
# display dendogram 
plot(fit)


groups <- cutree(fit, k=5) # cut tree into 5 clusters
# draw dendogram with red borders around the 5 clusters 
rect.hclust(fit, k=5, border="red")

plot(clust$wine,clust$heart)
text(x=clust$wine, y=clust$heart, labels=clust$country, col=groups)


# K means
clustk <- kmeans(clust[,c("wine","heart")], centers=3, nstart=10)
clustk

plot(clust$wine, clust$heart, xlab="wine", ylab="heart")
text(x=clust$wine, y=clust$heart, labels=clust$country,col=clustk$cluster+1)