MU Information Technology (Semester 6)
Data Mining & Business Intelligence
December 2012
Total marks: --
Total time: --
INSTRUCTIONS
(1) Assume appropriate data and state your reasons
(2) Marks are given to the right of every question
(3) Draw neat diagrams wherever necessary


Solve any four:-
1(a) What is noisy data. How to handle it?
5 M
1(b) What is Market Segmentation?
5 M
1(c) Explain fact less fact table with suitable example.
5 M
1(d) How FP tree is better than Apriori Algorithm.
5 M
1(e) Differentiate between Periodic Crawler and Incremental Crawler.
5 M

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

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

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

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

6(a) Explain HITS Algorithm in Web mining.
10 M
6(b) A database has four transactions. Let minimum support and confidence is 50%
10 M

Write short notes on any two :-
7(a) Issues in classification and explain any one technique of classification.
10 M
7(b) Sequence mining in transactional database.
10 M
7(c) Text mining approaches.
10 M
7(d) Fraud detection.
10 M



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