STUPIDSID
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}
{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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