Course: 05, 12
Time: 2.30 Hrs. M.M.: 75
Note: Attempt any 10 questions. 10 x 1 = 10
1. Data mining means to..........useful information from data with respect to the data model.
2. .........component of a data warehouse is responsible for the collection of data.
3. .........is simply defined as data about data.
4. .........is a classic data mining technique based on machine learning.
5. .........is a powerful tool capable of handling decision making and for forecasting future trends of the market?
6. Decision trees produce rules that are mutually............and collectively exhaustive with respect to the training database.
7. Statistical techniques may be employed for data mining at a number of stages of the mining process. (True/False)
8. OLAP stands for Online Address Processing Server. (True/False)
9. OLTP is based on the client-server architecture. (True/False)
10. A cluster is a group of objects that belong to the same class. (True/False)
11. Association technique is based on machine learning. (True/False)
12. Data cleaning involves removing the noise treatment of missing values. (True/False)
Note: Attempt any Ten questions. 5 x 6 = 30
13. Explain briefly problems and issues in data mining.
14. What is the difference between data mining and machine learning?
15. What do you understand by the term classification?
16. How the verification model differs from the discovery mode?
17. Explain data visualization. Why it is important?
18. What is a decision tree?
19. What are the characteristics of a data warehouse?
Note: Attempt any Ten questions. 5 x 7 = 35
20. Explain various applications of data mining.
21. Explain the process that contributes to data warehousing.
22. What do you understand by clustering analysis? What are the various types of clustering methods?
23. How neural network is applied in business?
24. How data mining improves telecommunication services
25. How can we integrate a data mining system with DB/DW system?
26. Describe OLAP and its various types.