hierarchical clustering with euclidean distance

Project Description:

14.4 marketing to frequent fliers. the file eastwestairlinescluster.xls contains information on 4000 passengers who belong to an airlines frequent flier program. for each passenger the data include information on their mileage history and on different ways they accrued or spent miles in the last year. the goal is to try to identify clusters of passengers that have similar characteristics for the purpose of targeting different segments for different types of mileage offers.
a. apply hierarchical clustering with euclidean distance and ward’s method. make sure to standardize the data first. how many clusters appear?
b. what would happen if the data were not standardized?
c. compare the cluster centroid to characterize the clusters and try to give each cluster a label.
d. to check the stability of the clusters, remove a random 5% of the data (by taking a random sample of 95% of the records), and repeat the analysis. does the same picture emerge?
e. use k-means clustering with the number of clusters that you found above. does the same picture emerge?
f. which clusters would you target for offers, and what type of offers would you target to customers in that cluster?
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