Download GTU B.Tech 2020 Summer 8th Sem 2170715 Data Mining And Business Intelligence Question Paper

Download GTU (Gujarat Technological University Ahmedabad) B.Tech/BE (Bachelor of Technology/ Bachelor of Engineering) 2020 Summer 8th Sem 2170715 Data Mining And Business Intelligence Previous Question Paper

Seat No.: ________
Enrolment No.___________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER? VIII EXAMINATION ? SUMMER 2020
Subject Code: 2170715 Date:02/11/2020
Subject Name: DATA MINING AND BUSINESS INTELLIGENCE
Time: 10:30 AM TO 01:00 PM Total Marks: 70
Instructions:
1. Attempt all questions.

2. Make suitable assumptions wherever necessary.

3. Figures to the right indicate full marks.
Q.1 (a) Define the following terms:
03
1) Data warehouse.
2) Business Intelligence
3) Metadata in Data warehouse

(b) List the major steps involved in the ETL process.
04

(c) Draw and explain the Data warehouse architecture.
07


Q.2 (a) Briefly discuss the schemas for multidimensional databases.
03

(b) Differentiate between OLTP and OLAP.
04

(c) Explain Various Data Mining Functionalities with an example.
07


OR


(c) Explain the different issues in data mining.
07
Q.3 (a) What is the need for preprocessing the data?
03

(b) How concept hierarchies are useful in data mining?
04

(c) Consider a transactional database where 1, 2, 3, 4, 5, 6, 7 are items.
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ID
ITEMS
T_1
1, 2, 3, 5
T_2
1, 2, 3, 4, 5
T_3
1, 2, 3, 7
T_4
1, 3, 6
T_5
1, 2, 4, 5, 6
Suppose the minimum support is 60%. Find all frequent itemsets using
Apriori algorithm.


OR

Q.3 (a) What is dimensionality reduction?
03

(b) Explain about Data Transformation method with suitable example.
04

(c) Discuss the variations of the Apriori algorithm to improve the efficiency.
07
Q.4 (a) What is meant by multidimensional association rules?
03

(b) Discuss the Information gain as attribute selection measure.
04

(c) Differentiate classification and prediction. State the issues regarding 07
classification and prediction.

OR

Q.4 (a) What is meant by Maximal Frequent Item Set?
03

(b) Discuss the Gain ratio as attribute selection measure.
04
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(c) Why is na?ve Bayesian classification called "na?ve"? Briefly outline the major 07
ideas of na?ve Bayesian classification.
Q.5 (a) List out the General applications of Clustering.
03

(b) What is Big Data? What is big data analytic?
04

(c) How the data mining will be used in the retail industry?
07


OR
Q.5 (a) Define the web mining.
03
(b) Discuss the main features of Hadoop Distributed File System.
04
(c) How the data mining will be used in the telecommunication industry?
07

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