Download JNTUH MCA 4th Sem R13 2018 June-July 814BD Data Warehousing And Data Mining Question Paper

Download JNTUH (Jawaharlal nehru technological university) MCA (Master of Computer Applications) 4th Sem (Fourth Semester) Regulation-R13 2018 June-July 814BD Data Warehousing And Data Mining Previous Question Paper

R13

Code No: 814BD

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

MCA IV Semester Examinations, June/July - 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) What are the characteristics of an interesting pattern?







[4]

b) What is meant by multi dimensional data model?









[4]

c) Give examples for a single dimensional association rule and a quantitative

multidimensional association rules.











[4]

d) What are the accuracy measures for a classifier?









[4]

e) List the merits and demerits of hierarchical agglomerative clustering.



[4]



PART - B

















5 ? 8 Marks = 40


2.

What is data mining? Explain it as a step in knowledge discovery process.

[8]

OR

3.

Demonstrate attribute subset selection as a preprocessing technique.



[8]



4.

Define data warehouse. Compare it with database management systems.

[8]

OR

5.

Explain BUC algorithm for data cube computation.







[8]


6.

Using FP Growth algorithm find frequent item sets(support threshold 30%) for the
following data:

















[8]

TID List of Items
1

Pen, eraser, marker, calculator, drafter

2

Pencil, marker, eraser, cutter

3

Pen, Pencil, eraser, A4 papers

4

A4 papers, CD, marker

5

Pencil, eraser, stapler, marker

6

Pen, eraser, sharpener, calculator

7

A4 papers, Pencil, eraser

8

Calculator, drafter, Pen

9

Pen, Pencil, CD, A4 papers.



OR

7.

What is correlation analysis? Explain the significance of lift measure for correlation
analysis.



















[8]









8.

How to prepare data for classification? Explain with suitable data set.



[8]

OR

9.

What are the characteristics of neural network that make a good classifier? Describe back
propagation algorithm.















[8]



10.

Explain k-means algorithm and contrast it with k-medoid algorithm.



[8]

OR

11.

What is an outlier? What is the need of outlier detection? Explain any one technique for
outlier analysis.

















[8]



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