K-medoids, K-means

Great Learning and lot of revisions needed to really deep dive and understand the fundamentals.

K-means

• Prone to outliers (Squared Euclidean gives greater weight to more distant points)
• Can't handle categorical data
• Work with Euclidean only

K-Medoids

• Restrict centre to data points
• Centre picked up only from data points
• We use same sum of squares for cost function but distance is not Euclidean distance
• Use your own custom distance functions when involved with numerical and categorical variables
• Example (25 languages, 24 columns, M/F/N - 2 columns) - Compute your own custom distance functions. It is one less because all zero combinations will also be treated as one attribute

Distance measure for numerical variables

• Euclidean based distance
• Correlation based distance
• Mahalanobis distance

Distance measure for category variables

• Matching coef and Jaquard’s coef

	#K medioids (Non-Hierarchical Clustering)
	#data frame
	food = read.csv("protein.csv")
	#Pass DF, Number of Clusters
	pam.result <- pam(food[,-1],2)
	pam.result$clustering
	summary(pam.result)
	#use manhattan measure
	#Pass DF, Number of Clusters
	#Argument diss=TRUE indicates that we use the dissimilarity matrix
	#Partitioning Around Medoids
	pam.result <- pam(food[,-1],k=2,diss=FALSE,metric="manhattan")
	pam.result$clustering
	summary(pam.result)
	plot(food$RedMeat, food$WhiteMeat, type="n", xlim=c(3,19), xlab="Red Meat",ylab="White Meat")
	text(x=food$RedMeat, y=food$WhiteMeat, labels=food$Country,col=pam.result$clustering+1)

view raw Kmediods.R hosted with ❤ by GitHub

Happy Learning!!!