RAJALAKSHMI ENGINEERING COLLEGE Thandalam

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Nov 20, 2013 (3 years and 9 months ago)

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RAJALAKSHMI ENGINEERING COLLEGE

Thandalam


Question Bank


Year

:

IV




Semester :

VII

Subject Code :

CS1004


Subject Name :

Data Warehousing and Mining

Subject Dept :

IT



Part A



( 2 marks )




Unit
-

I

1. Define Data mining.

2. Give some alternative terms for Data Mining.

3. What is
KDD?

4. What are th
e steps involved in KDD process?

5. What is the use of knowledge base?

6. Mention some of the data mining techniques?

7
. What is the purpose of Data mining Technique?

8
. Define Predictive model.

9
. Define Descriptive model?

10. Define cl
assification.

11. Define Prediction.

12. What is outlier mining?

13. Define Data characterization.

14. Define Data discrimination.

15.
Define data warehouse?

16
. What are operational databases?

17
. Define OLTP?

18
. Define OLAP?

19
. How a database design is

represented in OLTP system?

20
. How a database design is represented in OLAP system?

21
. Write short notes on multidimensional data model?

22
. Define data cube?

23
. What are facts?

24
. What are dimensions?

25
. Define dimension table?

26
. Define fact table
?

27
. What are lattice of cuboids?

28
. What is apex cuboid?

29
. List out the components of star schema?

30
. What is snowflake schema?

31
. List out the components of fact constellation schema?

32
. Point out the major difference between the star schema and s
nowflake schema model?

33
. Define concept hierarchy?

34
. List out the OLAP operations in multidimensional data model?

35
. What is roll
-
up operation?

36
. What is drill
-
down operation?

37
. What is slice operation?

38
. What is dice operation?

39
. What is pivo
t operation?

40
. List out the steps of the data warehouse design process?

41
. Define ROLAP?

42
. Define MOLAP?

43
. Define HOLAP?

44
. What is data mart?

45
. What is virtual warehouse?

46
. Define indexing?

47
. What are the types of indexing?

48
. Define metada
ta?

Unit II

1.

Define data preprocessing.

2.

What is meant by concept description?

3. Define data cleaning.

4. Define data reduction.

5. Define data transformation.

6. Define data integration.

7. What is binning?

8. How can correlation between two attribut
es be measured?

9. Define data aggregation.

10. What is meant by dimensionality reduction?

11. What are the primitives that define a task?

12. What is boxplot analysis?

13. What is DMQL?

14. List the different coupling schemes used in a data mining system.

15. What is entropy of a attribute?

16. Give the formula for mean, median and mode.

17. What is a loess curve?

18. What is a scatter plot?

19. What is a q
-
q plot?

Unit III

1. Define Association Rule Mining.

2. When we can say the association rules are int
eresting?

3. Explain Association rule in mathematical notations.

4. Define support and confidence in Association rule mining.

5. How is association rules mined from large databases?

6. Describe the different classifications of Association rule mining.

7. W
hat is the purpose of Apriori Algorithm?

8. What is boolean association rule?

9. How to generate association rules from frequent Item sets?

10. Give few techniques to improve the efficiency of Apriori algorithm.

11. What are the things suffering the perfor
mance of Apriori candidate generation
technique.

12. Describe the method of generating frequent item sets without candidate generation.

13. Define Iceberg query?

14. Mention few approaches to mining Multilevel Association Rules.

15. What are multidimension
al association rules?

16. What is quantitative association rule?

17. Define frequent itemset.

Unit IV

1. Define the concept of classification.

2
. What is Decision Tree?

3
. What is Attribute Selection Measure?

4
. Describe Tree pruning methods.

5
.

Define Pre
pruning.

6. Define Post pruning.

7
. Define the concept of Prediction.

8
. Define Clustering
.

9
. What do you mean by Cluster Analysis?

10
. What are the fields in which clustering techniques are used?

11.
What are the requirements of cluster analysis?

12
. Wha
t are the different types of data used for cluster analysis?

13
. What is interval scaled variables?

14
. Define Binary variables
.

And what are th
e two types of binary variables?

15
. Define nominal, ord
inal and ratio scaled variables.

16
. What
is

mean
t

by pa
rtitioning method?

17. Define CLARA and CLARANS.

18
. What is Hierarchical method?

19
. Differentiate Agglomerative and D
ivisive Hierarchical Clustering.

20
. What is CURE?

21. Define Chameleon method.

22. Define Density based method.

23
. What is a DBSCAN?

24
. What is
mean
t

by Grid Based Method?

25
. What is a STING?

26. Define Wave Cluster.

27
. What is Model based method?

28
. What is the use of Regression?

29
. What are the reasons for not using the linear regression model to estimate the output
data?

30. Why i
s naïve Bayesian classification is called ‘naïve’?


Unit


V


1. What are the classifications of tools for data mining?

2. What are commercial tools?

3. What are Public domain Tools?

4. What is the difference between generic single
-
task tools and generic m
ulti
-
task tools?

5. What are the areas in which data warehouses are used in present and in future?

6. Specify some of the sectors in which data warehousing and data mining are used?

7. Describe the use of DBMiner.

8. Applications of DBMiner.

9. Give some o
f the data mining tools.

10. Mention some of the application areas of Data mining.

11. Differentiate data query and Knowledge query.

12. Differentiate Direct Query answering and Intelligent query Answering.

13. Define Visual Data mining

14. What does Audio

Data Mining mean?

15. Specify the steps involved in DNA Analysis

16. What are the factors involved while choosing data mining system?

17
. Define Text mining
.

18
. What does web mining mean?

19
. Define spatial data mining.

20
. Explain Multimedia Data Mining
.

21. What kind of association can be mined from multimedia data?




Part B


( 16

marks )

Unit I

1. Explain the evoluti
on of Database technology?

2.
Explain the steps of knowledge discovery in databases?

3. Explain the architecture of data mining system?

4. Explain various tasks in data mining?

5. Explain the taxonomy of data mining tasks?

6. Explain various techniques in
data mining?

7. Explain the major issues in data mining.

8. Explain the classification of data mining systems.

9. Explain the data mining functionalities in detail.

10. Discuss the components of Data warehouse?

11. List out the difference between OLTP and
OLAP.

12. List out the major distinguishing features between OLTP and OLAP.

13. Discuss the various schematic representations in multidimensional model.

14. Explain the OLAP operations in multidimensional model?

15. Explain the design and construction of d
ata warehouse?

16. Explain the Three
-
tier data warehouse architecture?

17. Explain indexing?

18. Write short notes on metadata repository?

19. Write short notes on VLDB?


Unit II

1. Explain the need and steps involved in data preprocessing.

2. Describe how

concept hierarchies are useful in data mining.

3. List out and describe the primitives for specifying a data mining task.

4. Explain data integration in detail.

5. Explain data transformation methods in detail.

6. Explain data reduction in detail.

7. Expl
ain data discretization in detail.

8. Explain data mining query language in detail.

9. Explain the architecture of data mining systems in detail.

10. How graphical user interfaces are designed using DMQL?

11.
What is DMQL? Explain with an example

12. Expla
in data dispersion characteristics in detail.

13. Explain attribute oriented induction algorithm with example.

14. Explain class comparison methods in detail.

15. Explain analytical characterizatio
n in detail.


Unit II
I

1. Explain mining single
-
dimensional Boolean association rules from transactional
databases?

2. Explain apriori algorithm?

3. Explain how the efficiency of apriori is improved?

4. Explain frequent itemsets without candidate generatio
n?

5. Explain mining Multi
-
dimensional Boolean association rules from transactional
databases?


Unit IV

1. Explain the issues regarding classification and prediction?

2. Explain classification by Decision tree induction?

3. Write short notes on patterns?

4
. Explain regression in predictive modeling?

5. Explain statistical perspective in data mining?

6. Explain Bayesian classification?

7. Discuss the requirements of clustering in data mining?

8. Explain the various types of variables used in clustering?

9. E
xplain the partitioning method of clustering?

10. Explain the hierarchical method of clustering?

11. Explain the density
-
based method of clustering?

12. Explain the Grid
-
based method of clustering?

13. Explain the Model
-
based method of clustering?


Unit V

1. Explain Data mining applications for Biomedical and DNA data analysis?

2. Explain Data mining applications for Financial data analysis?

3. Explain Data mining applications for Retail industry?

4. Explain Data mining applications for the Telecommunicatio
n industry?

5. Explain DBMiner tool in data mining?

6. Explain how data mining is used in Health care analysis?

7. Explain how data mining is used in Banking Industry?

8. Explain the types of data mining?

9.
Explain
Spatial Database mining.

9.
Explain
Mult
imedia

Database mining.

10.
Explain
Text
Database mining.

11.

Explain the mining of time series and sequence data.