Download GTU (Gujarat Technological University) BE/BTech (Bachelor of Engineering / Bachelor of Technology) 2018 Winter 7th Sem New 2170715 Data Mining And Business Intelligence Previous Question Paper
1
Seat No.: ________ Enrolment No.___________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER ?VII (NEW) EXAMINATION ? WINTER 2018
Subject Code: 2170715 Date: 03/12/2018
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) What is Data Mining? Why is it called data mining rather knowledge
mining?
03
(b) Explain various features of Data Warehouse?
04
(c) Differentiate between Operational Database System and Data Warehouse
07
Q.2 (a) What is the difference between KDD and Data Mining?
03
(b)
What is Concept Hierarchy? List and briefly explain types of Concept
Hierarchy
04
(c) Explain Mean, Median, Mode, Variance, Standard Deviation & five number
summary with suitable database example.
07
OR
(c) What is noise? Explain data smoothing methods as noise removal technique
to divide given data into bins of size 3 by bin partition (equal frequency), by
bin means, by bin medians and by bin boundaries.
Consider the data: 10, 2, 19, 18, 20, 18, 25, 28, 22
07
Q.3 (a) Differentiate Fact table vs. Dimension table
03
(b) Suppose that the data for analysis includes the attribute age.
The age values for the data tuples are (in increasing order):
13, 15, 16, 16, 19, 20, 23, 29, 35, 41, 44, 53, 62, 69, 72
Use min-max normalization to transform the value 45 for age onto the range
[0:0, 1:0]
04
(c) Explain mining in following Databases with example.
1. Temporal Databases
2. Sequence Databases
3. Spatial Databases
4. Spatiotemporal Databases.
07
OR
Q.3 (a) List and describe methods for handling missing values in data cleaning. 03
(b) Explain the following as attribute selection measure:
(i) Information Gain
(ii) Gain Ratio
04
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1
Seat No.: ________ Enrolment No.___________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER ?VII (NEW) EXAMINATION ? WINTER 2018
Subject Code: 2170715 Date: 03/12/2018
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) What is Data Mining? Why is it called data mining rather knowledge
mining?
03
(b) Explain various features of Data Warehouse?
04
(c) Differentiate between Operational Database System and Data Warehouse
07
Q.2 (a) What is the difference between KDD and Data Mining?
03
(b)
What is Concept Hierarchy? List and briefly explain types of Concept
Hierarchy
04
(c) Explain Mean, Median, Mode, Variance, Standard Deviation & five number
summary with suitable database example.
07
OR
(c) What is noise? Explain data smoothing methods as noise removal technique
to divide given data into bins of size 3 by bin partition (equal frequency), by
bin means, by bin medians and by bin boundaries.
Consider the data: 10, 2, 19, 18, 20, 18, 25, 28, 22
07
Q.3 (a) Differentiate Fact table vs. Dimension table
03
(b) Suppose that the data for analysis includes the attribute age.
The age values for the data tuples are (in increasing order):
13, 15, 16, 16, 19, 20, 23, 29, 35, 41, 44, 53, 62, 69, 72
Use min-max normalization to transform the value 45 for age onto the range
[0:0, 1:0]
04
(c) Explain mining in following Databases with example.
1. Temporal Databases
2. Sequence Databases
3. Spatial Databases
4. Spatiotemporal Databases.
07
OR
Q.3 (a) List and describe methods for handling missing values in data cleaning. 03
(b) Explain the following as attribute selection measure:
(i) Information Gain
(ii) Gain Ratio
04
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2
(c) Explain three tier data warehouse Architecture in details.
07
Q.4 (a) How K-Mean clustering method differs from K-Medoid clustering method? 03
(b) Define data cube and explain 3 operations on it.
04
(c) State the Apriori Property. Generate large itemsets and association rules
using Apriori algorithm on the following data set with minimum support
value and minimum confidence value set as 50% and 75% respectively
TID Items Purchased
T101 Cheese, Milk, Cookies
T102 Butter, Milk, Bread
T103 Cheese, Butter, Milk, Bread
T104 Butter, Bread
07
OR
Q.4 (a) Define following terms :
Data Mart , Enterprise Warehouse & Virtual Warehouse
03
(b) Discuss the application of data warehousing and data mining
04
(c) What is web log? Explain web structure mining and web usage mining in
detail
07
Q.5 (a) Draw the topology of a multilayer, feed-forward Neural Network.
03
(b) Explain Linear regression with example.
04
(c) Explain the major issues in data mining 07
OR
Q.5 (a) Briefly explain text mining
03
(b) What is market basket analysis? Explain the two measures of rule
interestingness: support and confidence
04
(c) What is Big Data? What is big data analytic? Explain the big data-
distributed file system.
07
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This post was last modified on 20 February 2020