Reg. No. :

B.E./B.Tech. DEGREE EXAMINATION, NOVEMBER/DECEMBER 2011.

Seventh Semester

Computer Science and Engineering

CS 2032 — DATA WAREHOUSING AND DATA MINING

(Common to Sixth Semester Information Technology)

(Regulation 2008)

Time : Three hours Maximum : 100 marks

Answer ALL questions.

PART A — (10 × 2 = 20 marks)

1. What is a data mart?

2. List the three important issues that have to be addressed during data

integration.

3. What is a multi dimensional database?

4. What is an apex cuboid?

5. State the need for data cleaning.

6. What is pattern evaluation?

7. What is correlation analysis?

8. What is rule based classification? Give an example.

9. Define clustering.

10. What is an outlier? Mention its application.

PART B — (5 × 16 = 80 marks)

Question Paper Code : 55279

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55279 2

11. (a) What is a data warehouse? With the help of a neat sketch, explain the various components in a data warehousing system. (16)

Or

(b) What is a multiprocessor architecture? List and discuss the steps involved in mapping a data warehouse to a multiprocessor architecture. (16)

12. (a) (i) Distinguish between Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP). (4)

(ii) What is business analysis? List and discuss the basic features that are provided by reporting and query tools used for business analysis. (12)

Or

(b) Giving suitable examples, describe the various multi-dimensional schema. (16)

13. (a) (i) List and discuss the classification of data mining systems. (8)

(ii) List and discuss the steps for integrating a data mining system with a data warehouse. (8)

Or

(b) (i) What is the significance of interestingness measures in a data mining system? Give examples.

(ii) Describe the issues and challenges in the implementation of data mining systems.

14. (a) (i) What is classification? With an example explain how support vector machines can be used for classification. (10)

(ii) What are the prediction techniques supported by a data mining system? (6)

Or

(b) Apply the a priori algorithm to the following data set. State and discuss each step in the Apriori algorithm. Assume. (16)

Solution :

Trans ID

Items Purchased

101 Apple, Orange, Litchi, Grapes

102 Apple, Mango

103 Mango, Grapes, Apple

104 Apple, Orange, Litchi, Grapes

105 Pears, Litchi

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Trans ID

Items Purchased

106 Pears

107 Pears, Mango

108 Apple, Orange, Strawberry, Litchi, Grapes

109 Strawberry, Grapes

110 Apple, Orange, Grapes

The set of items is {Apple, Orange, Strawberry, Litchi, Grapes, Pears, Mango}. Use 0.3 for the minimum support value.

15. (a) What is grid based clustering? With an example explain an algorithm for grid based clustering. (16)

Or

(b) Consider five points { }54321 ,,,, XXXXX with the following

coordinates as a two dimensional sample for clustering :

( ) ( ) ( ) ( ) ( )2,6;1,5;1,5.1;0,0;5.2,5.0 54321 ===== XXXXX

Illustrate the K-means partitioning algorithms using the above data set. (16)

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