Exercise

Interpreting your clustering results

Great job figuring out that your data is best represented by six clusters! The fun comes now when you try to interpret your clustering results. This step should result in extracting valuable insights from these groups.

In this final exercise, you are going to calculate the average values of each customer attribute per cluster and label these cluster representatives according to your business needs.

The mall_scaled dataset and the dplyr package for data manipulation have been loaded for you.

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Cluster the mall_scaled data into six clusters and have K-means restart 20 times. Save your clustering results in the mall_6 variable.