M.Sc. DEGREE EXAMINATION, NOVEMBER 2017
Third Semester
Computer Science and Information Technology
Elective: DATA MINING AND WAREHOUSING
(CBCS 2014 onwards)
Time 3 Hours Maximum 75Marks
Part A (10 X 2 20)
Answer all questions.
1. Name any two data mining techniques.
2. List out any two steps in data mining process.
3. Define data cube.
4. What are data mart?
5. What is data pre-processing?
6. Define data reduction.
7. List the advantages of grid based cluster methods.
8. List out the disadvantages of outlier analysis.
9. List any two goals in time series analysis.
10. Mention the important uses of graph mining.
Sub. Code
4MCI3E4
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Part B X 5 25)
Answer all questions.
11. What is data mining? Explain it.
Or
Write a short notes on machine learning.
12. What is meant by data warehousing? Discuss it.
Or
Describe the concept of warehouse schema.
13. Give an account on data mining query language.
Or
Write a short note on association rule mining.
14. What is meant by classification? Discuss it.
Or
Discuss the concept of data propagation.
15. Write a note on outlier analysis.
Or
Write a short note on partitioning clustering
methods.
Part C X 10 30)
Answer any three questions.
16. What is KDD? List and explain the steps in data mining
process.
17. Write a brief note on Multidimensional data model.
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18. Describe the different methods for data cleaning.
Discuss the issues to be considered during data
integration.
19. Explain the different hierarchical and density based
clustering methods in detail.
20. Elaborate the concept of Web mining with its categories.
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