MU Information Technology (Semester 7)
Data Warehousing, Mining and Business Intelligence
May 2014
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
1 (a) What are major issue in data mining?
5 M
1 (b) Explain different OLAP operations.
5 M
1 (c) Difference between database and data warehouse.
5 M
1 (d) Write a short note on Linear regression.
5 M

2 (a) Explain constraint based and multilevel association rules with an example.
10 M
2 (b) Explain market basket analysis and uses of it.
10 M

3 (a) Explain BRICH method of clustering with an example.
10 M
3 (b) Explain Regression. Write short note on Non-Linear regression.
10 M

4 (a) Explain data cleaning, data transformation and Integration with an example.
10 M
4 (b) Apply Bayesin classification to predict class of new tuple (Nicol, Female, 1.67m). Use the following data.
Person ID Name Gender Height Class
1 Kristina Female 1.6 m Short
2 Jim Male 2 m Tall
3 Maggie Female 1.9 m Medium
4 Martha Female 1.85 m Medium
5 John Male 2.8 m Tall
6 Bob Male 1.7 m Short
7 Clinton Male 1.8 m Medium
8 Nyssa Female 1.6 m Short
9 Kathy Female 1.65 m Short
10 M

5 (a) What are outlier. Explain outlier analysis.
10 M
5 (b) Explain K-means clustering and solve the following with k=3
{2,3,6,8,9,12,15,18,22}
10 M

6 (a) Explain Bussiness Intelligence issues.
10 M
6 (b) Describe the steps involved in data mining when viewed as a process of Knowledge discovery.
10 M

Short note on any three
7 (a) Application of Web Mining
7 M
7 (b) Market segmentation
7 M
7 (c) Sequence Mining in Transaction
7 M
7 (d) Agglomerative clustering.
7 M



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