DATA WAREHOUSING FUNDAMENTALS: A Comprehensive Guide for IT Professionals

17.7. EXERCISES

1. Match the columns:

   knowledge discovery process OLAP cluster detection decision trees link analysis hidden layers genetic algorithms data warehouse MBR banking application

   reveals reasons for the discovery neural networks distance function feeds data for mining data-driven fraud detection user-driven forms groups highly iterative associations discovery

2. As a data mining consultant, you are hired by a large commercial bank that provides many financial services. The bank already has a data warehouse that it rolled out two years ago. The management wants to find the existing customers who are most likely to respond to a marketing campaign offering new services. Outline the knowledge discovery process, list the phases, and indicate the activities in each phase.

3. Describe how decision trees work. Choose an example and explain how this knowledge discovery process works.

4. What are the basic principles of genetic algorithms? Give an example. Use the example to describe how this technique works.

5. In your project you are responsible for analyzing the requirements and selecting a toolset for data mining. Make a list of the criteria you will use for the toolset selection. Briefly explain why each criterion is necessary.