CS 1004 – DATA WARE HOUSING AND MINING Question paper

2010 Anna University Chennai B.E Computer Science BE/B.Tech DEGREE EXAMINATION APRIL/MAY 2010 SIXTH SEMESTER COMPUTER SCIENCE AND ENGINEERING CS 1004 – DATA WARE HOUSING AND MINING Question paper


BE/B.Tech DEGREE EXAMINATION APRIL/MAY 2010
SIXTH SEMESTER
COMPUTER SCIENCE AND ENGINEERING
CS 1004 – DATA WARE HOUSING AND MINING
(REGULATION 2004)

TIME: THREE HOURS MAXIMUM MARKS :100 MARKS
Answer all the question PART –A(10*2=20)

1.What are the characteristics of data warehouse?
2.Define data model.
3.What are the various forms of the data processing?
4.State the significance of hierarchy of data.
5.what are the interestingness measures of association rule mining?
6.Define multi level association rule.
7.What are the factors to be considered, When comparing classification methods?
8.Mention the various types of data available in data mining.
9.Define spatial database.
10.List the some applications of data mining.

PART –B (5x16=80 marks)

11 (a) Explain Data ware house architecture and operational data stores with neat diagram.[Marks 16]
or
(b) Discuss the nine decisions in the design of data warehouse in detail. [Marks 16]

12 (a) Discus the five primitives for specifying task. [Marks 16]
or
(b) Discuss the importance of establishing a standardized data mining query language .What are the potential benefits and challenges involved in such a task? [Marks 16]

13 (a) Discuss the single dimensional boolean association rule mining for transaction database.[Marks 16]
or
(b) With an example discuss multilevel association rule. [Marks 16]

14 (a) Briefly discuss the major steps involved in the induction of decision trees using the ID# algorithm. [Marks 16]
or
(b) What is clustering?How does it differ from classification?Describe the following approaches to clustering methods,partitioning methods and hierarchical methods.Give an example for each [Marks 2+2+4+4+4=16]

15 (a) Describe the applications and trends in data mining in detail. [Marks 16]
or
(b) Write short notes on:
(i) Data mining for retail industry [Marks 8]
(ii) Visual and audio data mining [Marks 8]

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