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QP-1
OXFORD ENGINEERING COLLEGE
(Accredited by NAAC with ‘B’ Grade)
Pirattiyur, Trichy-09
DEPARTMENT OF INFORMATION TECHNOLOGY
MODEL EXAMINATION
CS2032 – Data warehousing and Data Mining
YEAR/SEM: III/VI
DURATION: 3 Hrs
DATE/SESSION: 07.04.2014 / A.N
MAX.MARKS:100
14. With relevant examples discuss multidimensional online
analytical processing and multi relational online analytical
processing.
(16)
15. How data mining systems are classified? Discuss each
classification with an example.
(16)
OR
16. How data mining system can be integrated with a data
warehouse? Discuss with example.
(16)
17. Discuss the apriori algorithm for discovering frequent item
sets. Apply to the following data set: use Minimum support
count 3.
(16)
PART A
Answer all the questions
(10X2=20 Marks)
1. How is a data warehouse different from database? How they
are similar?
2. Why data transformation is essential in the process of
knowledge discovery?
3. Classify OLAP tools.
4. What is an apex cuboid? Give example.
5. State why data preprocessing an important issue for data
warehousing and data mining?
6. What do data mining functionalities include?
7. With an example explain correlation analysis.
8. What is a support vector machine?
9. What is an outlier? Give example.
10. List any two data mining applications.
PART B - (5X16=80 Marks)
11. What is a data warehouse? Diagrammatically illustrate and
discuss the architecture.
(16)
OR
12. List and discuss the steps involved in mapping the data
warehouse to multi processor architecture.
(16)
13. List and discuss the basic features that are provided by
reporting and query tools used for business analysis.
(16)
OR
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vannila,apple
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OR
18. Develop an algorithm for classification using baysian
classification with example.
(16)
19. With example discuss partitioning based clustering.
OR
20. With example discuss constraint based clustering.
(16)
(16)
QP-2
OXFORD ENGINEERING COLLEGE
(Accredited by NAAC with ‘B’ Grade)
Pirattiyur, Trichy-09
DEPARTMENT OF INFORMATION TECHNOLOGY
MODEL EXAMINATION
CS2032 – Data warehousing and Data Mining
YEAR/SEM: III/VI
DURATION: 3 Hrs
DATE/SESSION: 07.04.2014 / A.N
MAX.MARKS:100
PART A
Answer all the questions
(10X2=20 Marks)
1. What is a data mart?
2. List the three important issues that have to be addressed
during data integration.
3. What is a multi dimensional database?
4. What is an apex cuboid?
5. Define clustering.
6. State the need for data cleaning.
7. What is pattern evaluation?
8. What is correlation analysis?
9. What is rule based classification? Give an example.
10. What is an outlier? Mention its application.
PART B - (5X16=80 Marks)
11. What is a data warehouse? With the help of a neat sketch,
explain the various components in a data warehousing
system.
(16)
OR
12. What is a multiprocessor architecture? List and discuss the
steps involved in mapping a data warehouse to a
multiprocessor architecture.
(16)
13. i) Distinguish between OLTP and OLAP.
(4)
ii) What is business analysis? List and discuss the basic
features that are provided by reporting and query tools used
for business analysis.
(12)
OR
14. Giving suitable examples, describe the various multidimensional schema.
(16)
15. i) List and discuss the classification of data mining systems. (8)
ii) List and discuss the steps for integrating a data mining system
with a data warehouse.
(8)
OR
16. i) What is the significance of interestingness measures in a data
mining system? Give examples.
(8)
ii) Describe the issues and challenges in the implementation of
data mining systems.
(8)
17. i) What is classification? With an example explain how support
vector machines can be used for classification.
(10)
ii) What are the prediction techniques supported by a data
mining system?
(6)
OR
18. Explain with example apriori algorithm.
(16)
19. What is grid based clustering? With an example explain an
algorithm for grid based clustering.
(16)
OR
20. Consider five points { X1,X2,X3,X4,X5} with the following
coordinates as a two dimensional sample for clustering :
Illustrate the K-means partitioning algorithms using the above
data set.
(16)