DATA WAREHOUSING, MINING AND BUSINESS INTELLIGENCE (DWMBI), B.E. (IT) Semester- 7, (December 2012)

DATA WAREHOUSING, MINING AND BUSINESS INTELLIGENCE (DWMBI)

B.E. (IT) Semester- 7, (December 2012)

Con. 8391-12.

KR-1089

(3 Hours)

[Total Mark 100]

N.B. : (1) Question No 1 is compulsory.

(2) Solve any four questions from remaining six questions.

1. Solve any four:-

(a) What is noisy data? How to handle it? --- (5 Marks)
(b) What is market segmentation? --- (5 Marks)
(c) Explain fact less fact table with suitable example. --- (5 Marks)
(d) How FP tree is better than Apriori Algorithm. --- (5 Marks)
(e) Differentiate between Periodic Crawler and Incremental Crawler. --- (5 Marks)

2. (a) Explain multidimensional association rules with suitable example. --- (10 Marks)
(b) Explain spatial data cube construction and spatial OLAP with example. --- (10 Marks)

3. (a) Explain Hoeffding Tree algorithm with example. --- (10 Marks)
(b) What is Web mining? Explain web content mining with reference to personalization harvest system. --- (10 Marks)

4. (a) What is clustering? Explain requirements and applications in detail. --- (10 Marks)
(b) Explain Agglomerative Clustering with an example. --- (10 Marks)

5. (a) Write difference between OLTP and OLAP explain different OLAP operations. --- (10 Marks)
(b) Explain regression? Explain Linear Regression with example. --- (10 Marks)

6. (a) Explain HITS Algorithm in Web mining. --- (10 Marks)
(b) A database has four transitions. Let minimum support and confidence is 50%. --- (10 Marks)
D=

Tid		Item
100	----------------------------------	1, 3, 4
200	----------------------------------	2, 3, 5
300	----------------------------------	1, 2, 3, 5
400	----------------------------------	2, 5
500	----------------------------------	1, 2, 3
600	----------------------------------	3, 5
700	----------------------------------	1, 2, 3, 5
800	----------------------------------	1, 5
900	----------------------------------	1, 3

7. Write short notes on any two: --- (20 Marks)
(a) Issues in classification and explain any one technique of classification

(b) Sequence mining in transactional database

(c) Text mining approaches

(d) Fraud detection

Also see Data warehousing, Mining and business intelligence question papers for May 2012