DW & DM

1. Explain data mining as a step in the process of knowledge discovery

2. Differentiate b/w operational data base systems and data ware house

3. Explain star snow fake, fact constellation schema

4. Write the syntax's for the following data mining primitives
(i) kinds of knowledge to be mined
(ii) Measures of pattern interestingness
(iii) Task relevant data

5. What is data pre-processing? Explain data cleaning?

6. Write algorithm for attribute oriented oriented induction and explain the steps involved?

7. (i) Briefly discuss about data integration
(ii) Briefly discuss about data reduction

8. (i) Difference b/w OLAP & OLTP
(ii) Functionalities of data mining

9. Suppose that data for analysis include the attribute age in increasing order 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 30, 33, 33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70. use smoothing by "bin mean" and smoothing by "bin boundary" to smooth data(bin depth 3)

10. Explain mining class comparisions with examples

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