Data Warehousing and Data Mining 2076

Group A

Attempt any Two questions.

1. Discuss the types of web mining. Explain why K-means is sensitive to outlier and how does K-Medoid minimize this issue.

2. Do pattern and information refer to same aspect? Justify. Differentiate between data warehouse and operational database.

3. List the problems of Apriori algorithm with its possible solutions. Consider the following transaction dataset.

Transaction_ID          Item_List

T1                                 {K, A, D, B}

T2                                 {D,A,C,E,B}

T3                                 {C,A,B,E}

T4                                 {B,A,D}

What association rules can be found in this set, if the minimum support is 3 and the minimum confidence is 80%.

Group B

Attempt any eight questions. 

Question no 13 is compulsory.

4.How classification plays significance role in data mining? Explain.

5. Are the information given by data mining is always useful? What are the issues in data warehousing and data mining?

6.Explain the four characteristics of data warehouse.

7. Explain the optimization techniques in data cube computation.

8. How multidimensional data model helps in retrieving information? Explain with suitable example. 

9. Compare the OLAP servers, ROLAP, MOLAP and HOLAP.

11. Differentiate between KDD and data mining.

12. What does data warehouse tuning mean? Describe the parameters.

13. Write short notes on (Any Two)

a. Evolution analysis

b. Decision trees

c. Text mining

d. Classification using Regression