STUPIDSID
MU Information Technology (Semester 7)
Data Warehousing, Mining and Business Intelligence
December 2013
Data Warehousing, Mining and Business Intelligence
December 2013
Attempt any four :-
1 (a)
Differentiate between OLAP and OLTP
5 M
1 (b)
What is noisy data? How to handle it.
5 M
1 (c)
Explain constraint based association rule mining.
5 M
1 (d)
Why is tree pruning useful in decision tree induction.
5 M
1 (e)
What is balanced score card.
5 M
2 (a)
Explain in details HITS algorithm in web mining.
10 M
2 (b)
What are issue regarding classification? Different between classification and prediction.
10 M
3 (a)
Explain Data Mining Premitives.
10 M
3 (b)
Give the architecture of Typical Data Mining System.
10 M
4 (a)
Consider the following database with minimum support count=60%. Find all frequent item set using Appriori and also generate strong association rules if minimum confidence =50%.
| TID | Items-brought |
| T1 | {M,O,N,K,E,Y} |
| T2 | {D,O,N,K,E,Y} |
| T3 | {M,A,K,E} |
| T4 | {M,U,C,K,Y} |
| T5 | {C,O,O,K,I,E} |
10 M
4 (b)
Explain multidimensional and multilevel association rules with an example.
10 M
5 (a)
What do you mean by preprocessing? Why it is required.
10 M
5 (b)
What is ELT process? Explain in detail giving emphasis on Data Transformation.
10 M
6 (a)
Explain Bayesian classification.
10 M
6 (b)
Explain periodic crawler and Incremental crawler.
10 M
Write short notes any two :-
7 (a)
Test Mining Approaches
10 M
7 (b)
Numerority reduction.
10 M
7 (c)
Data Discretization and Summarization.
10 M
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