Accepting the linkages Now Construct the G-code of the code and also download the linking files to it in a drive link

R code

# Determine number of clusters

wss <- (nrow(mydata)-1)*sum(apply(mydata,2,var))for (i in 2:15) wss[i] <- sum(kmeans(mydata,centers=i)$withinss)plot(1:15, wss, type="b", xlab="Number of Clusters",ylab="Within groups sum of squares")

# K-Means Cluster Analysis

fit <- kmeans(mydata, 5) # 5 cluster solution

# get cluster means

aggregate(mydata,by=list(fit$cluster),FUN=mean)

# append cluster assignment

mydata <- data.frame(mydata, fit$cluster)

# Single linkage:

cars.single.link <- hclust(dist.cars, method='single')

# Plotting the single linkage dendrogram:

plclust(cars.single.link, labels=row.names(cars.data), ylab="Distance")

# Opening new window while keeping previous one open

windows()

# complete linkage:

cars.complete.link <- hclust(dist.cars, method='complete')

# Plotting the complete linkage dendrogram:

plclust(cars.complete.link, labels=row.names(cars.data), ylab="Distance")

# Average linkage:

cars.avg.link <- hclust(dist.cars, method='average')

# Plotting the average linkage dendrogram:

plclust(cars.avg.link, labels=row.names(cars.data), ylab="Distance")

# Average Linkage dendrogram seems to indicate two major clusters,

# Single Linkage dendrogram may indicate three.

# Single Linkage Solution:

cut.3 <- cutree(cars.single.link, k=3)

# printing the "clustering vector"

cut.3

cars.3.clust <- lapply(1:3, function(nc) row.names(cars.data)[cut.3==nc])

# printing the clusters in terms of the car names

cars.3.clust