Download JNTUH MCA 4th Sem R13 2017 August 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 2017 August 814BD Data Warehousing And Data Mining Previous Question Paper

R13

Code No: 814BD

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

MCA IV Semester Examinations, August - 2017

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)

Explain data mining functionalities.









[4]

b)

What is data mart explain with example?









[4]

c)

What is the set of closed item sets?









[4]

d) Differentiate classification and prediction.







[4]

e)

Explain outlier analysis.













[4]



PART - B

















5 ? 8 Marks = 40

2.a)

Explain descriptive data summarization with suitable examples.

b)

Explain applications of data warehousing.







[4+4]

OR

3.a)

Explain how to integrate data mining system with database or data warehouse
system?

b)

Discuss concept hierarchy in brief.









[4+4]



4.

Explain a multi tired architecture of data warehouse with neat diagram.

[8]

OR

5.a)

Discuss discovery driven exploration of data cubes with example.

b)

Explain iceberg cube in detail.











[5+3]


6.

Explain AProiry algorithm to generate frequent item sets with suitable example.























[8]

OR

7.

Discuss mining frequent item sets without candidate generation with suitable
example.

















[8]


8.

Explain Na?ve Bayesian classifier with suitable example.



[8]

OR

9.a)

Explain different methods for evaluating accuracy of a classifier or predictor.

b)

Explain rule based classification with suitable example.





[4+4]


10.a) Explain partitioning around mediods with suitable example.
b) Explain constraint-Based cluster analysis.







[4+4]

OR

11.a) Explain over all frame work of chameleon.
b) Discuss wavelet transformation based clustering.







[4+4]



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