Download JNTUH (Jawaharlal nehru technological university) MCA (Master of Computer Applications) 4th Sem (Fourth Semester) Regulation-R13 2018 January 814BD Data Warehousing And Data Mining Previous Question Paper
R13
Code No: 814BD
JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD
MCA IV Semester Examinations, January - 2018
DATA WAREHOUSING AND DATA MINING
Time: 3 Hours
Max. Marks: 60
Note: This question paper contains two parts A and B.
Part A is compulsory which carries 20 marks. Answer all questions in Part A. Part B
consists of 5 Units. Answer any one full question from each unit. Each question carries
8 marks and may have a, b, c as sub questions.
PART - A
5 ? 4 Marks = 20
1.a)
Discuss the data smoothing techniques.
[4]
b)
Discuss the OLAP query processing.
[4]
c)
Discuss constraint-based Association mining.
[4]
d)
How does tree pruning work?
[4]
e)
Why wavelet transformation useful for clustering?
[4]
PART - B
5 ? 8 Marks = 40
2.
Write the syntax for the following data mining primitives:
a) The kind of knowledge to be mined.
b) Measures of pattern interestingness.
[4+4]
OR
3.a)
Briefly discuss the data mining functionalities.
b)
Briefly discuss the major issues in data mining regarding performance and diverse database
types.
[4+4]
4.a)
Justify the role of data cube aggregation in data reduction process with an example.
b)
Differentiate operational database systems and data warehousing.
[4+4]
OR
5.a)
What is data warehousing? Give their applications.
b)
Briefly discuss data warehouse architecture.
[4+4]
6.
Compare and contrast Apriori algorithm with frequent pattern growth algorithm. Consider
a data set apply both algorithms and explain the results.
[8]
OR
7.a)
Explain how concept hierarchies are used in mining multilevel association rule?
b)
Give the classification of association rules in detail.
[4+4]
8. a) Discuss the five criteria for the evaluation of classification and prediction methods.
b)
Explain how rules can be extracted from training neural networks.
[4+4]
OR
9.a)
Explain the hold out method for estimating classifier accuracy.
b)
Discuss Fuzzy set approach for classification.
[4+4]
10.a) Write k-Means and k-Medoids algorithms.
b) Explain COBWEB model.
[4+4]
OR
11.a) Explain about Statistical-based outlier detection and Deviation-based outlier detection.
b) Given two objects represented by the tuples(22,1,42,10) and(20,0,36,8)
i) Compute the Manhatten distance between the two objects.
ii) Compute the Euchidean distance between the two objects.
[4+4]
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This post was last modified on 17 March 2023