Reg. No.
B.E. B.Tech. DEGREE EXAMINATION, MAY 2017
Fifth Semester
Computer Science and Engineering
01UCS505- DATA WAREHOUSING AND DATA MINING
(Regulation 2013)
Duration: Three hours Maximum: 100 Marks
Answer ALL Questions.
PART A (10 x 2 20 Marks) 1. What is meant by metadata and data mart? 2. Why data transformation is essential in the process of Knowledge discovery. 3. What is reporting tool? List out the examples for manages query tools. 4. Mention the features of in business applications using OLAP.
5. Write the roles of noisy data in data preprocessing. 6. What is interestingness of a pattern? 7. How would you evaluate attribute selection measure? 8. State the interesting measures of an association rule. 9. Define: K-means partitioning.
10. What are the challenges of outlier detection?
Question Paper Code: 31255
2
31255
PART B x 16 80 Marks)
11. Explain about various steps involved for design and construction of Data Warehouses with three tier architecture diagram.
Or
Discuss about the concept of Mapping the data warehouse to a multiprocessor architecture.
12. Describe about the various OLAP operations in multidimensional model.
Or
Explain about the concept of multidimensional online analytical processing and multi relational online analytical processing with suitable example.
13. Describe about the kinds of data mining steps in the process of knowledge data discovery.
Or
Explain about the architecture of a typical data mining system with diagram.
14. Explain about a method that performs frequent item set mining by using the prior knowledge of frequent item set properties.
Or
Differentiate Classification and Prediction. Explain the issues regarding classification and prediction.
15. Describe about the categorization of major clustering methods.
Or
Briefly describe about the different approaches behind statistical based outlier detection, distance based outlier detection.