The file EastWestAirlinesCluster. xls contains information on 3999 passengers who belong to an airline's frequent flyer 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. Apply hierarchical clustering to the data to the normalized measurements.
Apply k-means clustering to the data to the normalized measurements.
Interpret the clusters formed in above questions
Which approach is better?
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 using both methods. Does the same picture emerge?
Which clusters would you target for offers, and what types of offers would you target to customers in that cluster?