M.Sc. DEGREE EXAMINATION, APRIL/MAY 2018.
Second Semester
Data Science
Paper I DATA WAREHOUSING AND MINING
2 21421-A
Time 3 Hours Max. Marks 70
SECTION — A
Answer any FIVE questions. 6 30 Marks)
Each questions carries 6 marks.
1. What is OLAP? Give the difference between OLAP and OLTP.
2. Explain the need for data smoothing during pre-processing and discuss data
smoothing by Binning.
3. What is KDD? With the help of a neat diagram explain the steps in the KDD
process.
4. Explain Information Gain, Gain Ratio and Gini Index.
5. Describe basic principle of Attribute Oriented Indication.
6. Explain Linear and Non-Linear Regression methods of Predictions.
7. Write about typical requirements of clustering in data mining.
8. Write about DBSCAN clustering algorithm.
SECTION — B
Answer ONE question from each Unit. 10 40 Marks)
Each questions carries 10 marks.
UNIT I
9. Explain the and 'Snowflake' schemas of data warehouse.
Or
10. Describe the methods for handling the missing values in data cleaning.

UNIT II
11. What is sampling? Explain different type of sampling techniques with example.
Or
12. Discuss in detail about major issues in data mining.
UNIT III
13. State the Apriori Property. Generate large itemsets and association rules using
Apriori algorithm on the following data set with minimum support value and
minimum confidence value set as 50% and 75% respectively.
TID Items Purchased
T101 Cheese, Milk, Cookies
T102 Butter, Milk, Bread
T103 Cheese, Butter, Milk, Bread
T104 Butter, Bread
Or
14. Explain the steps of the ID3 algorithm for generating Decision frees.
UNIT IV
15. Explain k-means and k-medoids algorithms of clustering.
Or
16. Explain about model based and grid based clustering algorithms.