1. Write a short note on the categories of data mining from data analysis point of
view
2. What is meant by concept description? How is it different from OLAP? 4 to 9
3. Define data generalization. Explain attribute‐oriented induction approach to the
categorization of the generalization methods
4. Write and explain the general method for attribute‐oriented induction 41 to 45
5. Write a brief analysis of the attribute‐oriented induction algorithm 45 to 47
6. Explain the two ways of data cube implementation of attribute‐oriented
induction
7. Explain the methods for presenting the descriptions generated by attributeoriented
induction. Use suitable examples and diagrams.
8. Explain about analytical characterization and analytical class comparison 67 to 77
9. Explain the steps in attribute‐relevance analysis for concept description. 78 to 82
10. Explain the steps in analytical characterization using a suitable example 83 to 91
11. Explain the general procedure for class comparison 92 to 98
12. Illustrate the procedure for class comparison 99 to 105
13. Describe and compare different methods for the presentation of the results of
analytical characterization and class comparison
14. Explain and illustrate different methods for the calculation of central tendency 124
15. Explain and illustrate different methods for measuring the dispersion of data 131 to
16. Explain about the different graph display methods of basic statistical class
descriptions
17. Discuss the issues involved in mining concept or class descriptions 152 to 163
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