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Gujarat Technological UniversitySummer 2023 Examination

GTU 3160714 Data Mining Summer 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 Hours10:30 AM – 1:00 PM
Paper Structure5 QuestionsWith internal OR choices
Jump toQ1Q2Q3Q4Q5

Question 1

14 MarksMedium
(a)
What is market basket analysis? Precisely explain the meaning of the
following association rule:
computer → antivirus_software [support = 60%, confidence = 60%]
3 Marks
(b)

In real-world data, tuples with missing values for some attributes are a common occurrence. List and describe various methods for handling this problem.

4 Marks
(c)

With the help of a suitable diagram, describe the steps involved in data mining when viewed as a process of knowledge discovery.

7 Marks

Question 2

14 MarksMedium
(a)

Give a short example to show that items in a strong association rule are not always interesting.

3 Marks
(b)

Briefly describe how partitioning technique may improve the efficiency of Apriori algorithm.

4 Marks
(c)

Discuss how frequent itemsets can be generated using FP-Growth algorithm with the help of the following transactions. Let minimum support threshold is 2. Transaction ID Item IDs T1 I1, I2, I5 T2 I2, I4 T3 I2, I3 T4 I1, I2, I4 T5 I1, I3 T6 I2, I3 T7 I1, I3 T8 I1, I2, I3, I5 T9 I1, I2, I3

7 Marks
OR OPTION
(c)

A database has the following six transactions. Transaction ID Items T1 HotDogs, Buns, Ketchup T2 Chips, Coke T3 Coke, Chips, HotDogs T4 Ketchup, Chips T5 Buns, HotDogs T6 HotDogs, Chips, Coke Find all frequent itemsets and also generate the strong association rules using Apriori algorithm. Let minimum support threshold is 33.34% and minimum confidence threshold is 60%.

7 Marks

Question 3

14 MarksMedium
(a)
Describe any three primitives for specifying a data mining task.
3 Marks
(b)

The following table shows the midterm and final exam grades obtained by students in a database course. x (Midterm exam) y (Final exam) 72 84 50 63 81 77 74 78 94 90 86 75 59 49 83 79 65 77 33 52 88 74 81 90 Use the method of least squares to find an equation for the prediction of a student’s final exam grade based on the student’s midterm grade in the course. Predict the final exam grade of a student who received 86 grade in the midterm exam.

4 Marks
(c)

What is noise? Describe the possible reasons for noisy data. Explain the different techniques to remove the noise from data.

7 Marks
OR OPTION
(a)

Discuss outlier analysis as a data mining functionality with the help of an example.

3 Marks
(b)

Explain how classification rules are extracted from a decision tree with the help of an example.

4 Marks
(c)

Explain in detail - min-max normalization method. Use this method to normalize the following group of data by setting min = 0 and max = 1. 200, 400, 600, 1000

7 Marks

Question 4

14 MarksMedium
(a)
Differentiate classification and clustering.
3 Marks
(b)
Discuss data matrix and dissimilarity matrix with respect to clustering.
4 Marks
(c)

Apply ID3 classification algorithm on the following data and construct a decision tree. Show all the stepwise calculations clearly. 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

7 Marks
OR OPTION
(a)

Discuss cross-validation method for evaluating the accuracy of a classifier.

3 Marks
(b)

How k-means clustering method differs from k-medoids clustering method? Discuss major drawbacks of k-means clustering method.

4 Marks
(c)

Predict class label of the tuple X = (age = youth, income = medium, student = yes, credit_rating = fair) with the help of Naive Bayesian classification method and the following data. Show all the stepwise calculations clearly. 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

7 Marks

Question 5

14 MarksMedium
(a)
Discuss web structure mining.
3 Marks
(b)
Discuss multimedia mining.
4 Marks
(c)

Suppose that the data mining task is to cluster the following eight points (with (x, y) representing location) into three clusters: A1(2, 10), A2(2,

5), A3(8,
4), B1(5,
8), B2(7,
5), B3(6,
4), C1(1,
2), C2(4,
9)The distance function is Euclidean distance. Suppose initially we assign A1, B1, and C1 as the center of each cluster, respectively. With the help of k-means algorithm calculate,
i)The three cluster centers after the first round execution
ii)The final three clusters
7 Marks
OR OPTION
(a)
Discuss agglomerative hierarchical clustering method in brief.
3 Marks
(b)
Explain the any four typical requirements of clustering in data mining.
4 Marks
(c)
What is web mining? Explain 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 (Summer 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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