Hierarchical Clustering

• Compute distance in every pair of cluster
• Merge nearest ones until number of clusters = number of clusters needed
• Entire process can be represented as dendrogram
• At the end of the algorithm dendogram is plotted

Measuring Distance between clusters

• Single (Minimum Distance between two pairs one from each clusters)
• Complete (Maximum  between two pairs one from each clusters)
• Average (Average of all possible pairs)

	library(cluster)
	#Avoid country name
	#use euclidean
	#complete linkage mechanism
	#diss - false as you are passing data frame
	foodagg=agnes(food[,-1],diss=FALSE,metric="euclidian", method="complete")
	plot(foodagg)
	#To get the required number of cluster
	cutree(foodagg,k=5)
	# Agglomerative hierarchical clustering (agnes)
	library(cluster)
	ah <- agnes(food[,-1])
	plot(ah)
	#Divisive hierarchical clustering (diana)
	library(cluster)
	dh <- diana(food[,-1])
	plot(dh)
	#Examples http://www.math.wustl.edu/~victor/classes/ma322/r-eg-28.txt

view raw Hierarchical.R hosted with ❤ by GitHub

Happy Learning!!!