Anna University Questions, CS514 DATA WAREHOUSING AND DATA MINING April May 2014, Computer Science and Engineering (CSE), Eighth Semester, Regulation 2004
Exam

B.E/B.Tech. (Full
Time) DEGREE END SEMESTER EXAMINATIONS

Academic
Year

April May 2014

Subject
Code

CS514 
Subject
Name

Data Warehousing and Data Mining 
Branch

Computer Science and Engineering

Semester

Eighth Semester

Regulation

2004

B.E
/ B.Tech. (Full Time) DEGREE END SEMESTER EXAMINATIONS, APRIL / MAY 2014
Computer Science
and Engineering
Eighth Semester
CS514
DATA WAREHOUSING AND DATA MINING
(Regulation 2004)
Time : 3 Hours Answer A L L Questions Max. Marks 100
PARTA
(10 x 2 = 20 Marks)
1. What are the data mining task
primitives?
2. What is metadata? Explain its
types.
3. Distinguish between noise and
outliers. Give suitable examples.
4. What are the advantages of dimensional
modeling?
5. How do you perform discretizations
of numeric attributes?
6. What is lazy learner? Give an
example.
7. What is rule based classification?
Give an example.
8. Difference between density and grid
clustering.
9. What is precision and recall?
10. What are the applications of data
mining?
PartB
(5* 16 = 80 Marks)
11. (i) What is data mining? Explain
in detail with the KDD process. (10)
(ii) How data warehouses systems
different from operational database systems and how are they similar? (6)
12 (a)
(i) What is data warehouse? Explain
threetier data warehouse architecture with a neat sketch. (8)
(ii) A data warehouse can be modeled
by either a star schema or a snowflake schema. Briefly describe the
similarities and the differences of the two models, and the analysis their
advantages and disadvantages. Give which might be more empirically useful and
state the reasons. (8)
(OR)
12 (b)
(i) How are concept hierarchies useful
in OLAP? Explain OLAP operations in the multidimensional data model. (8)
(ii) Suppose that a data warehouse
consists of the four dimensions data, spectator, location, and game, and the
two measures count and charge, where charge is the fare that a spectator pays
when watching a game on a given data. Spectator may be students, adults, or
seniors, with each category having its own charge rate.
(1) Draw a star schema diagram for the
data warehouse. (4)
(2) Starting with the base cuboid
[data, spectator, location, game], what specific OLAP operations should one
perform in order to list the total charge paid by student spectators GM_Place
in 2000. (4)
13 (a)
(i) List and discuss the steps for
integrating a data mining system with a data warehouse. (10)
(ii) A data set for analysis includes
only one attribute X = {7, 12, 5, 18, 5, 9, 13, 12,19, 7,12,12,13, 3, 4, 5,13} (6)
(1) What is the mean and median of the
data set X?
(2) Find the standard deviation for X.
(3) Find outliers in the data set X.
Discuss the results.
(OR)
13 (b)
(i) What is an association rule
mining? Apply the FPGrowth algorithm to find the strong association rules for
the transactional database given below. (12)
TID

ITEMS

01

A, B,
C, D

02

A, C,
D, F

03

C, D,
E,G

04

A, D,
F ,B

05

B,
C,G

06

D,
F,G

07

A, B,
G

Analyze misleading associations for
the rule set obtained in the above transactional database.
(ii) What are the essential difference
between association rules and decision rules? (4)
14 (a)
(i) What are the prediction techniques
supported by a data mining system. (4)
(ii) State the ID3 algorithm. Apply
the same to construct a decision tree for the data given below. (12)
(OR)
14 (b) The data mining task is to
cluster the following eight points into three clusters: A1
(2,10))A2(2,5),A3(8,4),B1 (5J8))B2(7I9))B3(6,4),C1 (1,2),C2(5,9). The distance
function is Euclidean distance. Suppose initially we assign A1, B1 and C1 as
the center of each cluster, respectively. Apply the Kmeans algorithm to show
the three cluster centers after the first round execution and the final three
clusters. (16)
15 (a)
(i) What is web mining? Differentiate
between web content mining, web structure mining, and web usage mining. What is
trend analysis? (12)
(ii) What are the differences between
visual data mining and data visualization? (4)
(OR)
15 (b) What are the major challenges
faced in bringing data mining related to market? Illustrate data mining issues
that may have a strong impact on the market and society. Discuss how to
approach such issue. (16)
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