STUPIDSID
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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