Anna University Trichy 2010 B.E Computer Science and Engineering /B.Tech , EMR/EMR CS1011 DATA WAREHOUSING AND DATA MINING (REGULATION 2007) - Question Paper

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B.E/B.Tech.DEGREE EXAMINATIONS,APRIL/MAY 2011

7th SEMESTER

COMPUTER SCIENCE AND ENGINEERING

CS1011 DATA WAREHOUSING AND DATA MINING


(REGULATION 2007)

Time: 3 hours Maximum: 100 marks

ans ALL ques..

PART A-(10*2=20 Marks)

1.How are organization using the info from data warehouse?

2.What is discovery-driven exploration of data cubes?

3.List the problems to be considered during data integration.

4.What do you mean by data generalization?

5.Show the iceberg query form.

6.What is single dimension association rule?

7.Distinguish ranging from supervised and unsupervised learning.

8.What is an outlier?

9.List the kinds of dimensions used in a spatial data cube.

10.What is sequential trend mining?


PART B-(5*16=80 Marks)

11. (a) discuss the typical architecture of data warehousing. (8)

(b) How are concept hierarchies useful in OLAP (8)

Or

12. (a) Write note on multidimensional data model. (8)

(b) What is meta data repository?Explain. (8)

13. (a) discuss the process of data transformation.(8)

(b) List and discuss the primitives for specifying a data mining task. (8)

or

14. (a) discuss the functional components of data mining graphical user interface. (8)

(b) discuss the algorithm for attribute-oriented induction. (8)

15. (a) discuss the methods used to improve the efficiency of Apriori algorithm. (8)

(b) elaborate multi level association rules? discuss. (8)

or

16. discuss the FP-growth algorithm.Illustrate with an example. (16)

17. (a) discuss the algorithm for inducing a decision tree from training samples. (8)

(b) define the methods for assessing classifier accuracy.(8)

or

18. (a) List and discuss the typical requirements of clustering in data mining.(8)

(b) discuss k-medoids algorithm.(8)


19. (a) discuss how to construct a data cube for multimedia data analysis.(8)

(b) define about likeness search in time series analysis .(8)

or

20 explain in detail about the applications of data mining.(16)

Earning:   Approval pending.