Question 1
(a)
What is the importance of data analysis?
3 Marks
(b)
What is data warehousing? List the characteristics of data warehouse.
4 Marks
(c)
Explain the three tier architecture of data warehouse.
7 Marks
Gujarat Technological UniversityWinter 2025 Examination
GTU 3161610 Data Warehousing and Mining Winter 2025 Paper Solution & PDF
B.E. · IT Engineering · Semester 6 · Subject Code: 3161610
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
Question 2
(a)
What is OLAP? How OLAP is differ from OLTP?
3 Marks
(b)
What is data cube? Explain rollup operation on data cube.
4 Marks
(c)
Explain different schema of data warehouse with suitable diagram.
7 Marks
OR OPTION
(c)
Define: mean, median, mode, variance and standard deviation.
7 Marks
Question 3
(a)
List different methods for data discretization and explain any one in detail.
3 Marks
(b)
Why data smoothing is required? Perform smoothing by bin means, by bin medians and by bin boundaries on the given data with bin size is 3. Consider the data for price (in dollars): 4, 8, 9, 15, 21, 21, 24, 25, 26, 28, 29, 33.
4 Marks
(c)
Explain the process of extracting the knowledge from the database.
7 Marks
OR OPTION
(a)
Explain Association Rules with Confidence & Support.
3 Marks
(b)
Write a note on data cleaning process for missing value and noisy data treatment.
4 Marks
(c)
Explain various methods for normalization.
7 Marks
Question 4
(a)
What are the limitations of the Apriori approach for mining?
3 Marks
(b)
What is data mining? How data mining techniques useful in super market?
4 Marks
(c)
State the Apriori Property. Find frequent item-sets and association rules using Apriori algorithm on the following data set with minimum support is 60% and minimum confidence=80%. Sr.No TID List of items 1 T100 M, O, N ,K, E, Y 2 T200 D, O, N, K, E, Y 3 T300 M, A, K, E 4 T400 M, U, C, K, Y 5 T500 C, O, O, K, I, E
7 Marks
OR OPTION
(a)
Give the difference between supervised and un-supervised learning.
3 Marks
(b)
Explain attribute selection measures in decision tree.
4 Marks
(c)
Explain Baye’s Theorem and Naïve Bayesian Classification.
7 Marks
Question 5
(a)
Give the importance of the pruning in association rule mining.
3 Marks
(b)
What is outlier? Discuss different methods for outlier detection.
4 Marks
(c)
Briefly explain Linear and Non-linear regression.
7 Marks
OR OPTION
(a)
Explain text mining in brief.
3 Marks
(b)
What is web mining? Explain types of web mining.
4 Marks
(c)
Explain k-mean clustering algorithm.
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 Warehousing and Mining (Winter 2025, B.E. · IT 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.