Homework 7 - Chapter 9 (Extra Credit)

Due: Tuesday March 11, 2008 at 5:00pm
Each question is worth 2 points.

This is an extra credit assignment so no late assignments will be accepted. The solutions will be posted shortly after 5pm on Tuesday so you may study them before the final.

  1. What is data mining and how does it differ from queries?
  2. What is a predictor?
  3. What is structured data? How does it differ from unstructed data?
  4. Describe the decision tree technique. How is it used for data mining?
  5. List two weaknesses of the decision tree method.
  6. Describe the Naive Bayesian Classifier.
  7. What is the purpose of using the m-estimate method for the Naive Bayesian Classifier?
  8. What does the k-Nearest Neighbor algorithm do?
  9. How does the k-Nearest Neighbor algorithm differ from clustering?
  10. How can the hidden layer of a neural network be used for data mining?