Question

Use the data mtcars in R:

1. Describe the data in R. (e.g., how many variables, types of variables).

2. Clean the data by deleting missing values.

3. Normalize the data/standardize the variables.

4. Determine the number of clusters.

5. Use k means cluster analysis.

6. Get cluster means.

7. Visualize the clustering result.

Answer 1

Solution-A:

Rcode:

dim(mtcars)
colnames(mtcars)

there were 32 observations and 11 columns.they are

mpg" "cyl" "disp" "hp" "drat" "wt" "qsec" "vs" "am" "gear"
"carb"

Solution-B

mydata <- na.omit(mtcars) # listwise deletion of missing
mydata <- scale(mydata)

3. Normalize the data/standardize the variables.

mydata <- scale(mydata)
mydata

4. Determine the number of clusters.

dim(mydata)
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=9 clusters

K-Means Cluster Analysis
fit <- kmeans(mydata, 9)