GUJARAT TECHNOLOGICAL UNIVERSITY
BE - SEMESTER-VII(NEW) EXAMINATION — SUMMER 2019
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Subject Code:2170715 Date:18/05/2019Subject Name:Data Mining and Business Intelligence
Time:02:30 PM TO 05:00 PM Total Marks: 70
Instructions:
1. Attempt all questions.
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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 04 nonvolatile collection of data — Justify.
(c) Consider following database of ten transactions. Let min_sup = 30% and min_confidence = 60%.
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TID items bought
T1 |pen, pencil A) Find all frequent itemsets using 05 Apriori algorithm.
T2 |book, eraser, pencil
T3 |book, chalk, eraser, pen B) Generate strong association rules. 02
T4 |chalk, eraser, pen
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T5 |book, pen, pencilT6 |book, eraser, pen, pencil
T7 |ink, pen
T8 |book, pen, pencil
T9 |eraser, pen, pencil
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T10 |book, chalk, pencil Q.2 (a) Discuss following terms. 03
1) Supervised learning 2) Correlation analysis 3) Tree pruning
(b) What is noise? Explain binning methods for data smoothing. 04
(c) Discuss data warehouse architecture in detail. 07
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OR(c) Write and discuss the algorithm which is used to generate frequent itemsets 07 using an iterative level-wise approach based on candidate generation.
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
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ORQ.3 (a) Discuss Big Data. 03
(b) Discuss possible ways for integration of a Data Mining system with a Database 04 or DataWarehouse system.
(c) Enlist data reduction strategies and explain any two. 07
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Q.4 (a) Discuss various layers of 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= 07 youth, income = medium, student = yes, credit_rating = fair) using following training dataset.
age income Student credit_rating buys_computer
youth high no Fair no
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youth high no excellent nomiddle aged high no fair yes
senior medium no fair yes
senior low yes fair yes
senior low yes excellent no
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middle aged low yes excellent Yesyouth medium no fair no
youth low yes fair yes
senior medium yes fair yes
youth medium yes excellent yes
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middle aged medium no excellent yesmiddle aged high yes fair yes
senior medium no excellent no
OR
Q.4 (a) Explain various conflict resolution strategies in rule based classification. 03
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(b) What is classification? Explain classification as a two step process with 04 diagram.(c) Discuss fraud detection and click-stream analysis using data mining. 07
Q.5 (a) Compare datamart 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 07 algorithm in detail.
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ORQ.5 (a) Discuss following terms. 03
1) DataNode 2) NameNode 3) Text mining
(b) Discuss attribute subset selection. 04
(c) Compare OLAP and OLTP in detail. 07
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