Flying blind

I was a bit worried that I didn't really know what would be on our next exam in Data Mining (coming this Tuesday). We've covered stuff, but it's not at all clear how it translates to testable material. With the prof out sick on Thursday, I'm left with this review sheet:

1. What, if any, is the connection between classification and clustering?
2. Explain the Naïve Bayesian classification method. How are continuous attributes handled in this method?
3. Study the Bayesian classification numerical examples from Chap 5 slides (Tan et al.)
4. Problem 11, page 344, Aggarwal
5. Problem 12, page 320, Tan et al.
6. Consider three variables A, B, C in the following Bayesian belief network: A→E→C with P(A) = 0.3, P(E|A) = 0.8, P(E|Ā) = 0.6, P(C|E) = 0.2, P(C|Ē) = 0.3. Find P(A|C).
7. In stochastic heuristics for optimum clustering (such as simulated annealing or genetic algorithm), a worse move is often accepted as a step (among many steps) in the search for an optimum cluster. What is the justification?
8. Describe (i) one method of representing (encoding) the solution, and (ii) one method of creating the “next” point from the “current” point in simulated annealing-based clustering.
9. Is the simulated annealing-based clustering method guaranteed to find the optimal clustering solution?

Ok, I can answer all those, but it still seems like I might be missing something. I guess we'll see in two days.