M.Sc. DEGREE EXAMINATION, NOVEMBER/DECEMBER 2017.
Third Semester
Data Science
Paper IV DATA MINING
2 21434
Time 3 Hours Max. Marks 70
PART — A
Answer any FIVE questions. 6 30 Marks)
Each question carries 6 marks.
1. With examples discuss the different attribute types.
2. Explain 3-tier data warehouse architecture.
3. Write short notes on frequent item set mining?
4. What is cluster analysis Explain?
5. Write short notes on prediction and regression.
6. What is Data ware house? How it is different from data mining?
7. Compare and contrast classification and clustering.
8. Explain about outlier analysis with example.
PART — B
Answer ONE question from each Unit. 10 40 Marks)
Each question carries 10 marks.
UNIT I
9. What is Data Mining? Explain the process of knowledge discovery in databases
with a neat diagram.
Or
10. Explain the major issues in data mining.

UNIT II
11. What is multi-dimensional data model? Explain the star and snowflake schemas in
detail with suitable example.
Or
12. Briefly discuss about data integration.
UNIT III
13. With an example explain the process of decision tree induction.
Or
14. Discuss frequent item set generation in the apriori algorithm.
UNIT IV
15. Explain in detail DBSCAN algorithm with example.
Or
16. Explain the k-means clustering algorithm, along with its strengths and
weaknesses.
—————————