Anna University Coimbatore 2010-6th Sem B.Tech Information Technology , b.e/ s: /e ,regulation-2007, , 070230060-data warehousing and mining (common to cse/it) - Question Paper

ANNA UNIVERSITY COIMBATORE
B.E/B.TECH. DEGREE EXAMINATIONS: MAY/JUNE 2010
REGULATION-2007
6th SEMESTER
070230060-DATA WAREHOUSING AND MINING
(COMMON TO CSE/IT)

TIME: three Hours Max.Marks:100

PART-A
(20×2=40 MARKS)
ans ALL ques.

1. What is data warehouse?
2. Differentiate fact table and dimension table.
3. Briefly explain the schemas for multidimensional Databases.
4. Compare OLTP and OLAP.
5. List the problems to be considered during data integration.
6. Write the strategies for data reduction.
7. Why is it important to have data mining query language?
8. Write the syntax for characterization.
9. List the techniques to improve the efficiency of Apriori algorithm.
10. Define support and confidence.
11. What is FP growth?
12. How mete rules are useful in Constraint-based association mining.
13. What is Bayesian theorem?
14. Why is tree pruning useful in decision tree induction?
15. Give the difference ranging from agglomerative and divisive hierarchial clustering.
16. Define Outliers. List different outliers detection approaches.
17. List out the methods for info retrieval.
18. What is web usage mining?
19. Give a few applications of data mining.
20. What is a time-series database?

PART-B
(5×12=60 MARKS)
ans ANY 5 ques.

21. Describe the data warehouse architecture with a neat diagram.
22. Explain the steps involved in Attribute-Oriented induction for Data Characterization.
23. Discuss how Apriori algorithm is used to obtain frequent itemsets with an example.
24. How does data classification work? explain the major steps of back propagation algorithm with an example.
25. What is clustering? List the kinds of clustering techniques and explain the partitioning methods in detail.
26. Describe the different data mining primitives for specifying a data mining task.
27.a) List out the different problems to be considered in data mining.
b) define how to evaluate the accuracy of the classifier and increase the classifier accuracy.
28. What is web mining? discuss how to identify authoritative web pages and automatic classification of web documents.

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Earning:   Approval pending.