1. Midterm Review General In-class midterm, Nov 11, 2002. 7-10 pm Close-book, no cheat sheet Simple numeric calculators are allowed

2. What is Covered? Chapters 1 to 7 inclusive Materials covered in lecture notes, and the corresponding materials in the textbook Understanding of materials, applications of data mining, and performing algorithms on small datasets are essential Memorization is not emphasized Numerical calculation is not important (the answer is 3/5+A where A is pi*23 is fine)

3. Types of Questions and Samples Understanding and conceptual questions. Example What is k-fold cross-validation? What are the advantages and disadvantages of using it? If you have a very small or very large dataset, do you use small k or large k? Will the testing accuracy increase when k increases? Why?

4. Applications of data mining Example: Let’s say you work in a bank, and you want to promote mutual fund sale to customers. Formulate this task as data mining task(s), and describe how to solve it with data mining software/algorithms.

5. Algorithms and Calculation Perform these algorithms/calculations on small dataset Apply OLAP operations on data cubes Methods for data smoothing, normalization, and discretization Attribute relevancy using information gain Statistical measures of data (mean, quartile, etc.) Apriori algorithm (basic and constrained version) Decision tree algorithm Naïve Bayes K-nearest neighbor algorithm