MU Information Technology (Semester 6)
Data Mining & Business Intelligence
December 2013
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


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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