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
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
