1. How is a data mining different from a database?
2. Present an example where data mining is crucial to the success of a business.
3. Explain the difference and similarity between discrimination and classification, between characterization and clustering, and between classification and regression
4. Describe three challenges to data mining a regarding data mining methodology and user interaction issue.
5. Outline the major research challenge of data mining in one specific application domain, such as stream/sensor data analysis, spatiotemporal data analysis, or bioinformatics.
6. Briefly outline how to compute the dissimilarity between objects described by the following:
(a) Nominal attributes
(b) Binary attributes
(c) Numeric attribues
7. Briefly outline how to compute the visualization techniques described by the following:
(a) Pixel-oriented
(b) Geometric-based
(c) Parallel coordinates
8. Define what is the data preprocessing and explain four steps.
9. In real-world data, tuples with missing values for some attributes are a common occurrence. Describe various methods for handling this problem.
10. Briefly outline the normalization methods and show an example.
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