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Gujarat Technological UniversityWinter 2023 Examination

GTU 3160714 Data Mining Winter 2023 Paper Solution & PDF

B.E. · Computer Engineering · Semester 6 · Subject Code: 3160714
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Total Marks70 MarksExternal theory exam
Passing Marks23 Marks33% minimum cutoff
Exam Duration2.5 Hours2:30 PM – 5:00 PM
Paper Structure5 QuestionsWith internal OR choices
Jump toQ1Q2Q3Q4Q5

Question 1

14 MarksMedium
(a)
Define Data Warehouse. State it’s features.
3 Marks
(b)
Differentiate between OLAP and OLTP.
4 Marks
(c)
Explain in detail different steps of KDD process.
7 Marks

Question 2

14 MarksMedium
(a)
Why to preprocess the data in data Mining?
3 Marks
(b)
Explain Binning method with the help of example.
4 Marks
(c)
Explain following terms related to Association Rule Mining:
Itemset, Support Count, support, and Association rule.
Transaction ID Items
1 Bread, Milk
2 Bread, Chocolate, Pepsi, Eggs
3 Milk, Chocolate, Pepsi, Coke
4 Bread, Milk, Chocolate, Pepsi
5 Bread, Milk, Chocolate, Coke
For given example find support & confidence for
{Milk, Chocolate} ⇒ Pepsi.
{Milk, Pepsi} → {Chocolate}
{Chocolate, Pepsi} → {Milk}
7 Marks
OR OPTION
(c)

Solve the following problem using Apriori algorithm. Find the frequent itemsets and generate association rules on this. Assume that minimum support threshold (s = 33.33%), minimum confident threshold (c = 60%), minimum support count=2. Transaction ID Items T1 Hot Dogs, Buns, Ketchup T2 Hot Dogs, Buns T3 Hot Dogs, Coke, Chips T4 Chips, Coke T5 Chips, Ketchup T6 Hot Dogs, Coke, Chips

7 Marks

Question 3

14 MarksMedium
(a)
Define the following terms in Data Transformation:
iSmoothing
iiNormalization
iiiDiscretization
3 Marks
(b)
Differentiate between Classification and Prediction.
4 Marks
(c)

Explain Decision Tree Classification algorithm with the help of example.

7 Marks
OR OPTION
(a)

Differentiate between supervised learning and unsupervised learning.

3 Marks
(b)
What is Regression? Explain Linear Regression in short.
4 Marks
(c)
Explain Naïve Bayes Classifier with example.
7 Marks

Question 4

14 MarksMedium
(a)
What do you mean by Tree Pruning? Explain with example.
3 Marks
(b)
Explain the following as attribute selection measure:
i)Information Gain
ii)Gain Ratio
4 Marks
(c)

What do you mean by learning-by-observation? Explain k-Means clustering algorithm in detail.

7 Marks
OR OPTION
(a)
Define Data Cube. Explain any two operations on it.
3 Marks
(b)

Differentiate between Partition method and Hierarchical method of Clustering.

4 Marks
(c)
What are the requirements of Clustering in Data Mining?
7 Marks

Question 5

14 MarksMedium
(a)

How K-Mean clustering method differs from K-Medoid clustering method?

3 Marks
(b)

Draw and explain the topology of a multilayer, feed-forward Neural Network.

4 Marks
(c)
Explain the major issues in data mining.
7 Marks
OR OPTION
(a)
Give difference between text mining and web mining.
3 Marks
(b)
Why Hadoop is important?
4 Marks
(c)

What is web log? Explain web structure mining and web usage mining in detail.

7 Marks
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About this Examination Paper & Attribution

Official Gujarat Technological University (GTU) examination paper and step-by-step solutions for Data Mining (Winter 2023, B.E. · Computer Engineering, Sem 6). Features complete 70-mark regular & remedial examination pattern, official marking distribution across all 5 questions, and direct 1-click official PDF download.

Transcribed for student exam preparation from Gujarat Technological University official examination archives. Questions, syllabus guidelines, and curriculum marking schemes remain the intellectual property of Gujarat Technological University.

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