BE(IT) Subject: DMDW Assignment III

MGM’s College of Engineering, Nanded.

Department of IT
Semester I (2012-13)
Class: BE(IT) Subject: DMDW Assignment III
___________________________________________________________________

1. Describe data mining primitives in short.

2. Explain the functional components required for data mining GUI.

3. Explain the architecture of data mining systems.

4. Write the differences between supervised learning and unsupervised learning.

5. What are the different Interestingness Measures for the patterns extracted by data mining?

6. What are the various forms of presenting and visualizing the discovered patterns?

7. Write and explain the DMQL for Characterization, Discrimination and Association.

8. Suppose that the data for analysis includes the attribute age. The age values for the data tuples are (in increasing order) 13, 15, 16, 16, 19, 20, 20, 21, 22, 22, 25, 25, 25, 25, 30, 33,33, 35, 35, 35, 35, 36, 40, 45, 46, 52, 70.

(a) What is the mean of the data? What is the median?

(b) What is the mode of the data? Comment on the data’s modality (i.e., bimodal, trimodal, etc.).

(c) What is the midrange of the data?

9. How Information Gain is calculated? Explain.

10. Define: 1) Support 2) Confidence 3) Frequent itemset.

11. State and explain the steps of apriori algorithm.

12. How to improve the efficiency of apriori algorithm ?

13. For the following transaction database, find the frequent itemsets

using apriori algorithm. (use support threshold as 2).
                   _____________________
                             Tid          Items
                    _____________________
                             10           A, C, D

                             20          B, C, E

                             30          A, B, C, E

                            40           B, E
                    _______________________

14. What are Iceberg queries? Explain with an example.

15. Discuss the Multilevel Association Rules mining for transaction

database.

16. Explain the different approaches for Multilevel Association Rules

mining for transaction database with an appropriate example.





Faculty Incharge: Hashmi S A

Assignment II DMDM BE-IT 2012-13

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

Class: BE(IT)_2012-13 Subject: DMDW Assignment I

MGM’s College of Engineering, Nanded.
Department of IT
Semester I (2012-13)
Class: BE(IT)       Subject: DMDW         Assignment I
________________________________________________________

1.   What is Data warehouse? Explain in detail.

2.   Discuss the characteristics of DW.

3.   Why data in data warehouse needs to be integrated? Explain.

4.   Give the comparison between DBMs and DW.

5.   What is data cube? Explain with an appropriate example.

6.   Data ware house data is non-volatile. Why?

7.   Explain Star schema with an appropriate example.

8.   Explain the DW applications.

9.   Write down the DMQL for star schema.

10. What is OLAP? Discuss in detail.

11. Explain OLAP operations with examples.

12.  Explain the benefits of using DW for the businesses.

13.  Explain the steps of data warehouse design process.

14.  What is concept hierarchy? Explain with an example.

15.  What is the difference between OLTP and OLAP? 

16.  Explain Snow-flake schema with an appropriate example.

 

Faculty Incharge: Hashmi S A