ELCTIVE –III
Model Paper
First Question is Compulsory
Answer any four from the remaining
Answer all parts of any Question at one place.
Time: 3 Hrs. Max. Marks: 70
1. Briefly discuss.
a. Correlation analysis for handling redundancy.
b. Discretization
c. Advantages of ROLAP and MOLAP
d. Ice-berg query.
e. Constraint –based rule mining
f. Scalability of an algorithm
g. Cross table reporting
h. Slicing operations
i. Reasons for data partitioning
j. Components of five-number summary
2. a) What is data mining? Briefly describe the components of a data mining
system.
b) What kinds of patterns can be identified in a data mining system?
3. a) Write the differences between operational database and data warehouse.
b) Briefly describe 3-tier Data warehouse architecture
4. a) Write different approaches to data transformation.
b) Propose an algorithm in pseudo-code for automatic generation of a concept
hierarchy for categorical data based on the number of distinct values of attributes
in the given schema.
5.a. Discuss the essential features of a typical data mining query language like
DMQL.
b. Consider association Rule below, which was mined from the student
database at Big-University:
Major(X,"science") status(X,"undergrad").
Suppose that the number of students at the unive rsity (that is, the number of
task-relevant data tuples) is 3000, that 56% of undergraduates at the
university major in science, that 64% of the students are registered in
programs leading to undergraduate degrees, and that 70% of the students are
majoring in science.
44
6.a. Compute the confidence and support of above rule.
b. Consider Rule below:
Major(X,"biology") status(X,"undergrad"). [17%,80%]
Suppose that 30% of science students are majoring in bioogy. Would you
consider Rule 2 to be novel with respect to Rule 1? Explain.
a. Discuss why attribute relevance analysis is needed and how it can be performed.
b. Outline a data cube-based incremental algorithm for mining analytical class
comparisons.
7. Write the A priori algorithm for discovering frequent item sets for mining
single-dimensional Boolean Association Rule and discuss various approaches
to improve its efficiency.
8.a. Discuss the back propagation algorithm for neural network-based classification of
data.
b. What are the different categories of clustering methods?
RELATED LINKS
ANDHARA UNIVERSITY MCA PAPERS
Placement Papers
Model Paper
First Question is Compulsory
Answer any four from the remaining
Answer all parts of any Question at one place.
Time: 3 Hrs. Max. Marks: 70
1. Briefly discuss.
a. Correlation analysis for handling redundancy.
b. Discretization
c. Advantages of ROLAP and MOLAP
d. Ice-berg query.
e. Constraint –based rule mining
f. Scalability of an algorithm
g. Cross table reporting
h. Slicing operations
i. Reasons for data partitioning
j. Components of five-number summary
2. a) What is data mining? Briefly describe the components of a data mining
system.
b) What kinds of patterns can be identified in a data mining system?
3. a) Write the differences between operational database and data warehouse.
b) Briefly describe 3-tier Data warehouse architecture
4. a) Write different approaches to data transformation.
b) Propose an algorithm in pseudo-code for automatic generation of a concept
hierarchy for categorical data based on the number of distinct values of attributes
in the given schema.
5.a. Discuss the essential features of a typical data mining query language like
DMQL.
b. Consider association Rule below, which was mined from the student
database at Big-University:
Major(X,"science") status(X,"undergrad").
Suppose that the number of students at the unive rsity (that is, the number of
task-relevant data tuples) is 3000, that 56% of undergraduates at the
university major in science, that 64% of the students are registered in
programs leading to undergraduate degrees, and that 70% of the students are
majoring in science.
44
6.a. Compute the confidence and support of above rule.
b. Consider Rule below:
Major(X,"biology") status(X,"undergrad"). [17%,80%]
Suppose that 30% of science students are majoring in bioogy. Would you
consider Rule 2 to be novel with respect to Rule 1? Explain.
a. Discuss why attribute relevance analysis is needed and how it can be performed.
b. Outline a data cube-based incremental algorithm for mining analytical class
comparisons.
7. Write the A priori algorithm for discovering frequent item sets for mining
single-dimensional Boolean Association Rule and discuss various approaches
to improve its efficiency.
8.a. Discuss the back propagation algorithm for neural network-based classification of
data.
b. What are the different categories of clustering methods?
RELATED LINKS
ANDHARA UNIVERSITY MCA PAPERS
Placement Papers
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