Download GTU (Gujarat Technological University) BE/BTech (Bachelor of Engineering / Bachelor of Technology) 2019 Summer 7th Sem New 2170715 Data Mining And Business Intelligence Previous Question Paper
Seat No.: ________ Enrolment No.___________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER ?VII(NEW) EXAMINATION ? SUMMER 2019
Subject Code:2170715 Date:18/05/2019
Subject Name:Data Mining and Business Intelligence
Time:02:30 PM TO 05:00 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) Explain cluster analysis and outlier analysis with example. 03
(b) A data warehouse is a subject-oriented, integrated, time-variant, and
nonvolatile collection of data ? Justify.
04
(c) Consider following database of ten transactions. Let min_sup = 30% and
min_confidence = 60%.
A) Find all frequent itemsets using
Apriori algorithm.
B) Generate strong association rules.
TID items bought
T1 pen, pencil
T2 book, eraser, pencil
T3 book, chalk, eraser, pen
T4 chalk, eraser, pen
T5 book, pen, pencil
T6 book, eraser, pen, pencil
T7 ink, pen
T8 book, pen, pencil
T9 eraser, pen, pencil
T10 book, chalk, pencil
05
02
Q.2 (a) Discuss following terms.
1) Supervised learning 2) Correlation analysis 3) Tree pruning
03
(b) What is noise? Explain binning methods for data smoothing. 04
(c) Discuss data warehouse architecture in detail. 07
OR
(c) Write and discuss the algorithm which is used to generate frequent itemsets
using an iterative level-wise approach based on candidate generation.
07
Q.3 (a) Which are the two measures of rule interestingness? Explain with example. 03
(b) Discuss Hash-based technique to improve efficiency of Apriori algorithm. 04
(c) Explain various data normalization techniques. 07
OR
Q.3 (a) Discuss Big Data. 03
(b) Discuss possible ways for integration of a Data Mining system with a Database
or DataWarehouse system.
04
(c) Enlist data reduction strategies and explain any two. 07
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Seat No.: ________ Enrolment No.___________
GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER ?VII(NEW) EXAMINATION ? SUMMER 2019
Subject Code:2170715 Date:18/05/2019
Subject Name:Data Mining and Business Intelligence
Time:02:30 PM TO 05:00 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) Explain cluster analysis and outlier analysis with example. 03
(b) A data warehouse is a subject-oriented, integrated, time-variant, and
nonvolatile collection of data ? Justify.
04
(c) Consider following database of ten transactions. Let min_sup = 30% and
min_confidence = 60%.
A) Find all frequent itemsets using
Apriori algorithm.
B) Generate strong association rules.
TID items bought
T1 pen, pencil
T2 book, eraser, pencil
T3 book, chalk, eraser, pen
T4 chalk, eraser, pen
T5 book, pen, pencil
T6 book, eraser, pen, pencil
T7 ink, pen
T8 book, pen, pencil
T9 eraser, pen, pencil
T10 book, chalk, pencil
05
02
Q.2 (a) Discuss following terms.
1) Supervised learning 2) Correlation analysis 3) Tree pruning
03
(b) What is noise? Explain binning methods for data smoothing. 04
(c) Discuss data warehouse architecture in detail. 07
OR
(c) Write and discuss the algorithm which is used to generate frequent itemsets
using an iterative level-wise approach based on candidate generation.
07
Q.3 (a) Which are the two measures of rule interestingness? Explain with example. 03
(b) Discuss Hash-based technique to improve efficiency of Apriori algorithm. 04
(c) Explain various data normalization techniques. 07
OR
Q.3 (a) Discuss Big Data. 03
(b) Discuss possible ways for integration of a Data Mining system with a Database
or DataWarehouse system.
04
(c) Enlist data reduction strategies and explain any two. 07
2
Q.4 (a) Discuss various layers of multilayer feed-forward neural network with
diagram.
03
(b) What is apex cuboid? Discuss drill down and roll up operation with diagram. 04
(c) Using Naive Bayesian classification method, predict class label of X = (age =
youth, income = medium, student = yes, credit_rating = fair) using following
training dataset.
age income Student credit_rating
Class:
buys_computer
youth high no Fair no
youth high no excellent no
middle_aged high no fair yes
senior medium no fair yes
senior low yes fair yes
senior low yes excellent no
middle_aged low yes excellent Yes
youth medium no fair no
youth low yes fair yes
senior medium yes fair yes
youth medium yes excellent yes
middle_aged medium no excellent yes
middle_aged high yes fair yes
senior medium no excellent no
07
OR
Q.4 (a) Explain various conflict resolution strategies in rule based classification. 03
(b) What is classification? Explain classification as a two step process with
diagram.
04
(c) Discuss fraud detection and click-stream analysis using data mining. 07
Q.5 (a) Compare data mart and data warehouse. 03
(b) Discuss star schema and fact constellation schema with diagram. 04
(c) What do you mean by learning-by-observation? Explain k-Means clustering
algorithm in detail.
07
OR
Q.5 (a) Discuss following terms.
1) DataNode 2) NameNode 3) Text mining
03
(b) Discuss attribute subset selection. 04
(c) Compare OLAP and OLTP in detail. 07
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This post was last modified on 20 February 2020