Question: Suppose that IBM employs a network of expert analytics consultants for various projects. To help it determine how to distribute its bonuses, IBM wants to form groups of employees with similar performance according to key performance metrics. Each observation (corresponding to an employee) in the file Big Blue consists of values for
(1) Usage Rate, which corresponds to the proportion of time that the employee has been actively working on high priority projects,
(2) Recognition, which is the number of projects for which the employee was specifically requested, and
(3) Leader, which is the number of projects on which the employee has served as project leader.
Apply k-means clustering with for values of k 5 2, . . . ,7. Be sure to Normalize input data, and specify 50 iterations and 10 random starts in Step 2 of the XLMiner k-Means Clustering procedure. How many clusters do you recommend using to categorize the employees? Why?