MU Information Technology (Semester 6)
Data Mining & Business Intelligence
May 2017
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


1(a) Explain multilevel association rules with example.
10 M
1(b) Explain business intelligence issues.
10 M

2(a) Explain BIRCH algorithm with example.
10 M
2(b) Explain data mining steps in KDD. Explain its architecture.
10 M

3(a) Explain different visualization techniques that can be used in data mining.
10 M
3(b) Explain classifier accuracy evaluation techniques.
10 M

4(a) Define classification, its issues and explain ID3 algorithm.
10 M
4(b) Expalin K-mean clustering with suitable example.
10 M

5(a) Design BI system for fraud detection. Explain All steps from data collection to decision making.
10 M
5(b) Explain box plot summary with example.
10 M

6(a) Explain data preprocessing phase for data mining.
10 M
6(b) Explain Outlier analysis.
10 M



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