Download JNTUH MCA 4th Sem R13 2019 December 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 2019 December 814BD Data Warehousing And Data Mining Previous Question Paper






R13

Code No: 814BD

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

MCA IV Semester Examinations, December - 2019

DATA WAREHOUSING AND DATA MINING

S



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 is data preprocessing? What are the various forms of it.





[4]

b) Explain the architecture of data warehouse.









[4]

c) Distinguish between Apriori algorithm and Frequent pattern growth algorithm.

[4]

d) What are the steps involved in preparing the data for classification?



[4]

e) What are the advantages and disadvantages of k-means algorithm.



[4]



PART - B

















5 ? 8 Marks = 40

2.a)

Explain about the Integration of a Data mining system with a Database or Data
warehouse system.

b)

List the techniques for Data cleaning.











[4+4]











OR

3.a)

What are the issues in Data mining?

b)

Explain about the classification of data mining systems.







[4+4]



4.

Explain in detail about the implementation of a data warehouse.





[8]

OR

5.

Illustrate OLAP operations in the Multidimensional Data Model.



[8]


6.

Discuss the FP-growth algorithm. Explain with an example.





[8]

OR

7.

Discuss about mining multilevel association rules from transaction databases in detail.

























[8]


8.

Explain in detail about classification of Back propagation algorithm.



[8]

OR

9.

Explain linear regression and non-linear regression with example.



[8]


10.

Write algorithms for k-medoid clustering and Explain with simple data.

[8]

OR

11.

Explain about the Statistical-based outlier detection.







[8]



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This post was last modified on 17 March 2023