6) Describe cluster analysis and some of its applications.

7) Describe the k-nearest neighbor (kNN) data mining algorithm.

8) List the pros and cons of model ensembles compared to individual models.

9) What might happen if we trained a neural network using data that is unrelated to the predicted (dependent) variable. For example, using financial ratios to predict how many words are in the company's mission statement. Will the neural network cre ate a model? Thoroughly explain your answer.

10) Describe the type of decision situation that might lend itself toward simulation. What are simulation's pros and cons? In general terms, how would you go about use of simulation (What steps would you take before, during, and after while using simulation to make a decision?)?