Are you looking for model/sample, old/previous/last years question papers of Data Mining for M.G University, Kerala? Hereunder, you will find such model question paper for 5th (fifth) semester MCA degree examination. MCA 504 is the subject code of Data Mining. Look below to collect the contents of the question paper to use for your upcoming exams.
Mahatma Gandhi University
MCA Degree Examination
Model Question Paper
(2011 Revised Syllabi)
Fifth semester
MCA 504 DATA MINING
Time : Three hours
Maximum : 75 Marks
Part A
Answer any ten questions.
Each question carries 3 marks.
1. What are Data warehouses?
2. Explain Snowflakes and Fact Constellations.
3. Define hierarchical clustering.
4. What is outlier analysis?
5. Explain the features of Density-based clustering methods.
6. Compare and contrast classification and clustering.
7. Briefly discuss frequent item set mining.
8. Describe the Data Cube technology with an example.
9. Explain the issues related to classification and prediction.
10. Discuss any 3 data mining task primitives.
11. List any six applications of data mining.
12. Discuss major issues of data mining. (10 x 3 = 30 marks)
Part B
All questions carry equal marks.
13.a) What is Decision Tree Induction? Explain basic algorithm for inducing a decision tree from training tuples.
OR
b) What is prediction? Discuss the use of regression techniques for prediction?
14.a) Describe the methods for the generation of concept hierarchies for categorical data?
OR
b) Describe the features of partition based clustering algorithms.
15.a) Describe the various functionality of Data mining as a step in the process of knowledge discovery.
OR
b) Explain the Apriori algorithm. Also explain how the association rules are generated from frequent item sets.
16.a) Write and explain any one algorithm for classification.
OR
b) Discuss briefly the different steps in preprocessing of data.
17.a) Briefly discuss the architecture of data mining system.
OR
b) What is meant by dimensionality reduction? Discuss any 2 methods.
(5 x 9 = 45 marks)
Mahatma Gandhi University
MCA Degree Examination
Model Question Paper
(2011 Revised Syllabi)
Fifth semester
MCA 504 DATA MINING
Time : Three hours
Maximum : 75 Marks
Part A
Answer any ten questions.
Each question carries 3 marks.
1. What are Data warehouses?
2. Explain Snowflakes and Fact Constellations.
3. Define hierarchical clustering.
4. What is outlier analysis?
5. Explain the features of Density-based clustering methods.
6. Compare and contrast classification and clustering.
7. Briefly discuss frequent item set mining.
8. Describe the Data Cube technology with an example.
9. Explain the issues related to classification and prediction.
10. Discuss any 3 data mining task primitives.
11. List any six applications of data mining.
12. Discuss major issues of data mining. (10 x 3 = 30 marks)
Part B
All questions carry equal marks.
13.a) What is Decision Tree Induction? Explain basic algorithm for inducing a decision tree from training tuples.
OR
b) What is prediction? Discuss the use of regression techniques for prediction?
14.a) Describe the methods for the generation of concept hierarchies for categorical data?
OR
b) Describe the features of partition based clustering algorithms.
15.a) Describe the various functionality of Data mining as a step in the process of knowledge discovery.
OR
b) Explain the Apriori algorithm. Also explain how the association rules are generated from frequent item sets.
16.a) Write and explain any one algorithm for classification.
OR
b) Discuss briefly the different steps in preprocessing of data.
17.a) Briefly discuss the architecture of data mining system.
OR
b) What is meant by dimensionality reduction? Discuss any 2 methods.
(5 x 9 = 45 marks)
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