Compute the mean of each variables by clusters using original data for each model

INSTRUCTIONS TO CANDIDATES

ANSWER ALL QUESTIONS

(1) Load and attach the “mtcars” dataset. Conduct an exploration of what variables it contains and what the values look like (if you don’t know about any variables, find out what they are. If you don’t know about a car, use Google to find out what it is. Have a comprehensive understanding of what the cars are in your dataset). (0.5 Points)

It contains 32 observations and 11 variables: 

(2) Use the variables “mpg” and “cyl” to perform K-means clustering on the cars (get a subset of data including only “mpg” and “cyl” variables for the k-mean clustering). Run kmeans function with K=3 and 4 respectively. Which one is better? Use the ratio of betweenness and within sum of squared distance (=betweenness / tot.withinss) (1.5 Points)

(3) Compute the mean of each variables by clusters using original data for each model

K= 3

K = 4

(4) Drawing a plot to visually represent the clustering analysis results

K= 3

K = 4

(5) Get a subset of data including “disp” and “hp” variables. Using the subset of data, run kmeans clustering analysis with K= 3, 4, 5, and 6 respectively. Which value of K is better now among 4 models? Use the Elbow method for this one.(2 Points) 

(6) You have now used two sets of inputs (“mpg” and “cyl”; “disp” and “hp”). Which of the two sets of inputs gives better clusters? (Hint : You need to use the same evaluation metric to compare them. You can restrict to comparing for K=3 and 4) (2 Points)

Attachments:

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