3. Present an example where data mining is crucial to the success of a business. What data mining functionalities does this business need (e.g., think of the kinds of patterns that could be mined)? Can such patterns be generated alternatively by data query processing or simple statistical analysis?

4. Based on your observation, describe another possible kind of knowledge that needs to be discovered by data mining methods? Does it require a mining methodology that is quite different from those already discussed in class?

5. Outliers are often discarded as noise. However, one persons garbage could be anothers treasure. For example, exceptions in credit card transactions can help us detect the fraudulent use of credit cards. Using fraudulence detection as an example, propose two methods that can be used to detect outliers and discuss which one is more reliable.