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MU Information Technology (Semester 7)
Data Warehousing, Mining and Business Intelligence
December 2012
Data Warehousing, Mining and Business Intelligence
December 2012
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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