Download PTU (I.K. Gujral Punjab Technical University Jalandhar (IKGPTU) ) MBA (Master of Business Administration) 2020 March 3rd Sem 76987 Data Mining For Business Decisions Previous Question Paper
Roll No. Total No. of Pages : 02
Total No. of Questions : 17
MBA (2018 Batch) (Sem.?3)
DATA MINING FOR BUSINESS DECISIONS
Subject Code : MB 941-18
M.Code : 76987
Time : 3 Hrs. Max. Marks : 60
INSTRUCTIONS TO CANDIDATES :
1. SECTION-A contains EIGHT questions carrying TWO marks each and students
has to attempt ALL questions.
2. SECTION-B consists of FOUR Subsections : Units-I, II, III & IV. Each Subsection
contains TWO questions each carrying EIGHT marks each and student has to
attempt any ONE question from each Subsection.
3. SECTION-C is COMPULSORY and consist of ONE Case Study carrying TWELVE
marks.
SECTION-A
1. Explain Linear Regression briefly.
2. Define support and confidence in Association Rule Mining.
3. Briefly discus the working of Genetic Algorithms.
4. What is a Data Cube?
5. What is correlation? How is it measured?
6. Differentiate between Clustering and Classification.
7. Define hierarchical clustering.
8. Differentiate between MOLAP and HOLAP.
SECTION-B
UNIT-I
9. Explain OLAP and its types. Discus various OLAP operations.
10. Explain the concept of Star, Snowflake and Galaxy schema with the help of suitable
examples.
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1 | M-76987 (S32)-1905
Roll No. Total No. of Pages : 02
Total No. of Questions : 17
MBA (2018 Batch) (Sem.?3)
DATA MINING FOR BUSINESS DECISIONS
Subject Code : MB 941-18
M.Code : 76987
Time : 3 Hrs. Max. Marks : 60
INSTRUCTIONS TO CANDIDATES :
1. SECTION-A contains EIGHT questions carrying TWO marks each and students
has to attempt ALL questions.
2. SECTION-B consists of FOUR Subsections : Units-I, II, III & IV. Each Subsection
contains TWO questions each carrying EIGHT marks each and student has to
attempt any ONE question from each Subsection.
3. SECTION-C is COMPULSORY and consist of ONE Case Study carrying TWELVE
marks.
SECTION-A
1. Explain Linear Regression briefly.
2. Define support and confidence in Association Rule Mining.
3. Briefly discus the working of Genetic Algorithms.
4. What is a Data Cube?
5. What is correlation? How is it measured?
6. Differentiate between Clustering and Classification.
7. Define hierarchical clustering.
8. Differentiate between MOLAP and HOLAP.
SECTION-B
UNIT-I
9. Explain OLAP and its types. Discus various OLAP operations.
10. Explain the concept of Star, Snowflake and Galaxy schema with the help of suitable
examples.
2 | M-76987 (S32)-1905
UNIT-II
11. Explain the steps in Knowledge Discovery in databases.
12. a. List the different applications of Data Mining.
b. Briefly discus the different Descriptive Data Mining tasks.
UNIT-III
13. Explain Association Rule Mining with the help of suitable examples.
14. What is Bayes Theorem? Show how it is used for classification.
UNIT-IV
15. Describe K means clustering with an example.
16. Explain any two Hierarchical Clustering methods.
SECTION-C
17. Suppose that the data for analysis includes the attribute age. The age values for the data
tuples are (in increasing order) 13,15, 16,16,19,20, 20,21,22, 22, 25,25,
25,25, 30, 33, 33, 35, 35, 35, 36,40,45,46, 52, 70.
a. What is the mean of the data? What is the median?
b. What is the mode of the data?
c. What is the midrange of the data?
d. Can you find (roughly) the first quartile (Q1) and the third quartile (Q3) of the data?
NOTE : Disclosure of Identity by writing Mobile No. or Making of passing request on any
page of Answer Sheet will lead to UMC against the Student.
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This post was last modified on 22 March 2020