MGM’s College of Engineering, Nanded.
Department of IT
Semester I (2012-13)
Class: BE(IT) Subject: DMDW Assignment II
________________________________________________________
1. What is data mining? Explain its characteristics.
2. Describe data mining techniques in detail.
3. What is KDD? Enlist and explain the stages of KDD.
4. Discuss the goals of data mining.
5. What are the differences between KDD and DM? Explain in detail.
6. Discuss the applications of data mining.
7. Why preprocess the data for data mining?
8. Briefly discuss the forms of data preprocessing.
9. Explain the basic methods of data cleaning.
10. What are the issues in data integration in DM preprocessing.
11. What are the different data transformation techniques used in DM?
12. Suppose the minimum and maximum values for the attribute income are Rs. 25000 and Rs. 85000, respectively. Using min-max normalization, transform and map value Rs. 71600 to the range [0.0, 1.0].
13. The mean and standard deviation of the values for the attribute income are Rs. 41000 and Rs. 8000, respectively. Using z-score normalization transform a value of Rs. 62000.
14. Why data reduction technique is applied to data set in DM preprocessing? What strategies are used for data reduction?
15. What is dimensionality reduction? Explain its techniques with examples.
16. What is the difference between lossy and lossless data compression?
****************************All The Best*********************************
Faculty Incharge: Hashmi S A
Department of IT
Semester I (2012-13)
Class: BE(IT) Subject: DMDW Assignment II
________________________________________________________
1. What is data mining? Explain its characteristics.
2. Describe data mining techniques in detail.
3. What is KDD? Enlist and explain the stages of KDD.
4. Discuss the goals of data mining.
5. What are the differences between KDD and DM? Explain in detail.
6. Discuss the applications of data mining.
7. Why preprocess the data for data mining?
8. Briefly discuss the forms of data preprocessing.
9. Explain the basic methods of data cleaning.
10. What are the issues in data integration in DM preprocessing.
11. What are the different data transformation techniques used in DM?
12. Suppose the minimum and maximum values for the attribute income are Rs. 25000 and Rs. 85000, respectively. Using min-max normalization, transform and map value Rs. 71600 to the range [0.0, 1.0].
13. The mean and standard deviation of the values for the attribute income are Rs. 41000 and Rs. 8000, respectively. Using z-score normalization transform a value of Rs. 62000.
14. Why data reduction technique is applied to data set in DM preprocessing? What strategies are used for data reduction?
15. What is dimensionality reduction? Explain its techniques with examples.
16. What is the difference between lossy and lossless data compression?
****************************All The Best*********************************
Faculty Incharge: Hashmi S A
