Download GTU (Gujarat Technological University) BE/BTech (Bachelor of Engineering / Bachelor of Technology) 2019 Winter 7th Sem New 2170715 Data Mining And Business Intelligence Previous Question Paper
Seat No.: ________ Enrolment No.___________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER ? VII (New) EXAMINATION ? WINTER 2019
Subject Code: 2170715 Date: 30/11/2019
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.
MARKS
Q.1 (a) Define data mining and list its features. 03
(b) Differentiate between OLTP and OLAP. 04
(c) Describe the steps involved in data mining when viewed as a process of
knowledge discovery.
as a ras a process of knowle
07
Q.2 (a) Can BI is used for DM? Or vice versa? Justify. 03
(b) Explain in detail the extract/transform/load (ETL) design of an automated
warehouse.
04
(c) Explain Mean, Median, Mode, Variance, Standard Deviation & five number
summary with suitable database example.
07
OR
(c) Is Graphical visualization is better than text data ?Justify your answer and
explain different data visualization technique.
07
Q.3 (a) Explain why data warehouses are needed for developing business solutions
from today?s perspective. Discuss the role of data marts.
03
(b) Draw and Explain Snowflakes and Fact constellations Schema. 04
(c) Define outlier analysis? Why outlier mining is important? Briefly describe
the different approaches: statistical-based outlier detection, distance-based
outlier detection and deviation-based outlier detection.
07
OR
Q.3 (a) Discuss Following: (i) Meta Data (ii) Virtual Warehouse 03
(b) Briefly outline the major steps of decision tree classification. Why tree
pruning useful in decision tree induction?
04
(c) In data pre-processing why we need data smoothing? Discuss data smoothing
by Binning.
07
Q.4 (a) Draw the topology of a multilayer, feed-forward Neural Network. 03
(b) Describe Concept Hierarchy? List and briefly explain types of Concept
Hierarchy
04
(c) In real-world data, tuples with missing values for some attributes are a
common occurrence. Describe various methods for handling this problem.
07
OR
Q.4 (a) Define ?clustering?? Mention any two applications of clustering. 03
(b) Briefly explain Linear and Non-linear regression. 04
(c) Consider the following dataset and find frequent item sets and generate
association rules for them using Apriori Algorithm.
07
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1
Seat No.: ________ Enrolment No.___________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER ? VII (New) EXAMINATION ? WINTER 2019
Subject Code: 2170715 Date: 30/11/2019
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.
MARKS
Q.1 (a) Define data mining and list its features. 03
(b) Differentiate between OLTP and OLAP. 04
(c) Describe the steps involved in data mining when viewed as a process of
knowledge discovery.
as a ras a process of knowle
07
Q.2 (a) Can BI is used for DM? Or vice versa? Justify. 03
(b) Explain in detail the extract/transform/load (ETL) design of an automated
warehouse.
04
(c) Explain Mean, Median, Mode, Variance, Standard Deviation & five number
summary with suitable database example.
07
OR
(c) Is Graphical visualization is better than text data ?Justify your answer and
explain different data visualization technique.
07
Q.3 (a) Explain why data warehouses are needed for developing business solutions
from today?s perspective. Discuss the role of data marts.
03
(b) Draw and Explain Snowflakes and Fact constellations Schema. 04
(c) Define outlier analysis? Why outlier mining is important? Briefly describe
the different approaches: statistical-based outlier detection, distance-based
outlier detection and deviation-based outlier detection.
07
OR
Q.3 (a) Discuss Following: (i) Meta Data (ii) Virtual Warehouse 03
(b) Briefly outline the major steps of decision tree classification. Why tree
pruning useful in decision tree induction?
04
(c) In data pre-processing why we need data smoothing? Discuss data smoothing
by Binning.
07
Q.4 (a) Draw the topology of a multilayer, feed-forward Neural Network. 03
(b) Describe Concept Hierarchy? List and briefly explain types of Concept
Hierarchy
04
(c) In real-world data, tuples with missing values for some attributes are a
common occurrence. Describe various methods for handling this problem.
07
OR
Q.4 (a) Define ?clustering?? Mention any two applications of clustering. 03
(b) Briefly explain Linear and Non-linear regression. 04
(c) Consider the following dataset and find frequent item sets and generate
association rules for them using Apriori Algorithm.
07
2
minimum support count is 2 minimum confidence is 60% .
Q.5 (a) Differentiate Fact table vs. Dimension table 03
(b) What is market basket analysis? Explain the two measures of rule
interestingness: support and confidence
04
(c) Briefly explain the life-cycle of Data Analytics and discuss the role of data
scientists.
07
OR
Q.5 (a) Explain text mining using example. 03
(b) Explain data mining application for fraud detection. 04
(c) Discuss the main features of Hadoop Distributed File System. 07
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This post was last modified on 20 February 2020