Gujarat Technological UniversityWinter 2025 examination

GTU 3160714 Data Mining Winter 2025 Question Paper & PDF

B.E. · Computer Engineering · Semester 6 · Subject code 3160714
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Total
70 marks
To pass
23 marks (33%)
Duration
2.5 Hours 2:30 PM – 5:00 PM
Questions
5 with internal choice
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9 of 25 questions on this paper were also asked in other years. Each is marked below with the years it appeared.See all 27 repeated 3160714 questions, most asked first

Q.1

14 Marks
(a)
3 marks
What is data mining? List out the data mining functionality.
(b)
4 marksAsked in 4 of 7 papers
Briefly explain the issues in data mining.

Also asked in Summer 2026 Q2c, Summer 2024 Q1c, Winter 2023 Q5c

(c)
7 marksAsked in 4 of 7 papers

Explain frequent item-set mining algorithm with suitable example.

Also asked in Summer 2026 Q4c, Summer 2025 Q3c, Summer 2024 Q3c OR

Q.2

14 Marks
(a)
3 marksAsked in 2 of 7 papers
Explain k-fold cross validation.

Also asked in Summer 2023 Q4a OR

(b)
4 marks
Discuss dimensionality reduction techniques.
(c)
7 marksAsked in 7 of 7 papers
Explain KDD process.

Also asked in Summer 2026 Q1c, Summer 2025 Q1b, Winter 2024 Q1c, Summer 2024 Q1b, Winter 2023 Q1c, Summer 2023 Q1c

(c)
7 marks
Explain data pre-processing functionalities.

Q.3

14 Marks
(a)
3 marks
What is spatial data mining? Explain with its application.
(b)
4 marks

Discuss support, confidence, frequent item-set and strong association rules.

(c)
7 marksAsked in 5 of 7 papers
Explain decision tree algorithm with suitable example.

Also asked in Summer 2025 Q4c, Winter 2024 Q3c, Summer 2024 Q4c, Winter 2023 Q3c

(a)
3 marksAsked in 3 of 7 papers
Explain classification Vs. Prediction.

Also asked in Winter 2024 Q4a OR, Winter 2023 Q3b

(b)
4 marks

Explain proximity measures used in distance based classifiers.

(c)
7 marks
Explain ANN.

Q.4

14 Marks
(a)
3 marks
What is missing values and noise? How to handle them?
(b)
4 marks
Differentiate linear and logistic regression.
(c)
7 marks
Explain hierarchical and agglomerative method of clustering.
(a)
3 marks
What is closed item-set and maximal frequent item-set?
(b)
4 marks
Define accuracy, precision, recall and G-mean.
(c)
7 marks

Discuss spatial Data Mining, Temporal Mining, and Multimedia Mining with their application.

Q.5

14 Marks
(a)
3 marksAsked in 4 of 7 papers
Briefly explain outlier analysis.

Also asked in Summer 2025 Q5b, Winter 2024 Q4b OR, Summer 2023 Q3a OR

(b)
4 marksAsked in 3 of 7 papers
Differentiate clustering and classification.

Also asked in Winter 2024 Q4a, Summer 2023 Q4a

(c)
7 marks
Explain K-means algorithm.
(a)
3 marks
How to evaluate clustering algorithm?
(b)
4 marks
Discuss application of web mining.
(c)
7 marksAsked in 2 of 7 papers
Explain PAM algorithm.

Also asked in Winter 2024 Q5c

More Data Mining papers

Questions repeat across sessions. Working through two or three papers shows you which topics GTU asks most.

Summer 202670 marks · 5 questions
Summer 202570 marks · 5 questions
Winter 202470 marks · 5 questions
Summer 202470 marks · 5 questions

Other Semester 6 subjects

BE Computer

116AG01
Deep Learning and Neural Network
2160701
Software Engineering
2160704
Theory of Computation
2160707
Advanced Java

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About this paper

Official Gujarat Technological University (GTU) examination paper for Data Mining (Winter 2025, 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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