Data Warehousing And Mining Apr/May 2010

M.C.A. DEGREE EXAMINATION,APRIL/MAY 2010.
                                  Elective
MC 1630 -- DATA WAREHOUSING AND DATA MINING
                            (Regulation 2005)
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PART-A (10*2= 20 MARKS)

1.Define data mining.
2.state the relation of Data mining to statistics.
3.What is Data transformation?
4.Define: data consolidation, integration.
5.what's is a Decision tree?
6.Give the advantage of hierarchical over non-hierarchical clustering techniques.
7.List the benefits of Data Warehousing.
8.Write the types of parallelism and the various database architecture for parallel Processing.
9.State the four main categories of data mining products.
10.what is meant by feature reduction?

PART- B (5*16= 80 marks)

11.(a).Explain the architecture of a Data mining system.(Or)
(b).Write short note on the following data mining techniques:Decision Tree,
Visualization techniques, Genetic algorithm, Clustering technique.

12.(a).Discuss the Data Cleaning Phase with suitable examples.(Or)
(b).Write a detailed note for mining association rules in large databases.

13.(a).Explain the various hierarchical clustering techniques.(Or)
(b).State the rule of neutral networks in data mining in detail.

14.(a).With a neat diagram,explain the data warehouse architecture.(Or)
(b).Discuss the categorization of OLAP tools.

15.(a).Explain a case study of Data mining using DBminor.(Or)
(b).state the processes involved in mining text and spatial databases.

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