Exam Details
Subject | data mining | |
Paper | ||
Exam / Course | m.c.a.computer applications | |
Department | ||
Organization | loyola college (autonomous) chennai – 600 034 | |
Position | ||
Exam Date | April, 2018 | |
City, State | tamil nadu, chennai |
Question Paper
1
LOYOLA COLLEGE (AUTONOMOUS), CHENNAI 600 034
M.C.A.DEGREE EXAMINATION -COMPUTER APPLICATIONS
FIFTH SEMESTER APRIL 2018
CA 5807- DATA MINING
Date: 18-04-2018 Dept. No. Max. 100 Marks
Time: 09:00-12:00
Part-A
Answer ALL Questions (10 20)
1. What is Data mining?
2. What do you mean Data integration?
3. What are the issues in Classification?
4. What do you mean by Prediction?
5. Draw any decision tree for a class attribute.
6. Write an example for Density based method.
7. Define Outlier analysis.
8. What is Vision Based Page segmentation?
9. What is Web Data mining?
10. Give few examples of Data mining tools.
Part B
Answer ALL Questions 40)
11. Explain KDD process in detail.
Write short notes on OLAP operations.
12. Describe if-then rule based classification with an example.
Explain linear regression for Prediction.
13. Explain the requirements of Clustering in Data mining.
Explain Graph based clustering method.
14. Write short notes on Text mining.
Write short notes on mining Multimedia data.
15. Explain the Applications of Data mining for Biological data Analysis
Explain the features of R-miner.
2
Part C
Answer any TWO Questions 20= 40)
16. Discuss all the functions of Data mining 20 marks.
17. Explain Classification by Decision tree algorithm in detail.
Describe Hierarchical clustering.
18. Discuss Spatial data mining.
Discuss Single association rule generation with an example.
LOYOLA COLLEGE (AUTONOMOUS), CHENNAI 600 034
M.C.A.DEGREE EXAMINATION -COMPUTER APPLICATIONS
FIFTH SEMESTER APRIL 2018
CA 5807- DATA MINING
Date: 18-04-2018 Dept. No. Max. 100 Marks
Time: 09:00-12:00
Part-A
Answer ALL Questions (10 20)
1. What is Data mining?
2. What do you mean Data integration?
3. What are the issues in Classification?
4. What do you mean by Prediction?
5. Draw any decision tree for a class attribute.
6. Write an example for Density based method.
7. Define Outlier analysis.
8. What is Vision Based Page segmentation?
9. What is Web Data mining?
10. Give few examples of Data mining tools.
Part B
Answer ALL Questions 40)
11. Explain KDD process in detail.
Write short notes on OLAP operations.
12. Describe if-then rule based classification with an example.
Explain linear regression for Prediction.
13. Explain the requirements of Clustering in Data mining.
Explain Graph based clustering method.
14. Write short notes on Text mining.
Write short notes on mining Multimedia data.
15. Explain the Applications of Data mining for Biological data Analysis
Explain the features of R-miner.
2
Part C
Answer any TWO Questions 20= 40)
16. Discuss all the functions of Data mining 20 marks.
17. Explain Classification by Decision tree algorithm in detail.
Describe Hierarchical clustering.
18. Discuss Spatial data mining.
Discuss Single association rule generation with an example.
Other Question Papers
Subjects
- .net technologies
- .net technologies lab
- advanced .net
- advanced java
- c++ and data structures lab
- cloud computing
- computer graphics and multimedia applications
- computer graphics and multimedia lab
- computer organization and architecture
- data communication and networks
- data mining
- database administration
- database management systems
- database management systems lab
- discrete structures
- free and open source software development
- it infracture management
- java programming lab
- knowledge management system andapplications
- microprocessor and its applications
- mobile computing
- network administration
- network security
- network security lab
- neural networks using matlab
- neural networks using matlab lab
- object-oriented software engineering
- operating systems
- programming and data structures through c++
- programming with java
- resource management techniques
- software development lab
- software project management
- software testing
- statistical methods for computer applications
- unix programming lab
- xml and web services
- xml and web services lab