DATA WAREHOUSING, MINING AND BUSINESS INTELLIGENCE (DWMBI), B.E. (IT) Semester- 7, (December 2011)

DATA WAREHOUSING, MINING AND BUSINESS INTELLIGENCE (DWMBI)

B.E. (IT) Semester- 7, (December 2011)

Con. 6150-11

MP-5732

(3 Hours)

[Total Mark 100]

N.B. : (1) Question No 1 is compulsory.
(2) Solve any four from remaining six questions.

1. (a) A Manufacturing company has a huge sales network. To control the sales, it is divided in the regions. Each region has multiple zones. Each zone has different cities. Each sales person is allotted different cities. The object is to track sales figure at different granularity levels of region and also to count the number of products sold. Create both data warehouse schema to take into consideration of above granularity levels for region, Sales person and the quarterly, yearly and monthly sales. ---- (10 Marks)
(b) Compare database and data warehouse. --- (5 Marks)
(c) Explain Business Intelligence issues. --- (5 Marks)

2. (a) What are the major issues in Data Mining? --- (5 Marks)
(b) Explain BIRCH method of clustering with an example. --- (5 Marks)
(c) Explain data integration and Transformation with an example. --- (10 Marks)

3. (a) Explain techniques of Web Structure Mining. --- (10 Marks)
(b) Explain the KDD process in detail. --- (10 Marks)

4. (a) How to FP tree is better than Apriori Algorithm. --- (5 Marks)
(b) A database has four transitions. Let minimum support and confidence is 50%
D=

Tid	Items
100	1, 3, 4
200	2, 3, 5
300	1, 2, 3, 5
400	2, 5

Find out frequent item sets and strong association rules for above example. --- (5 Marks)
(c) Explain constraint based association rule mining. --- (5 Marks)
(d) Explain multilevel association rules. --- (5 Marks)

5. (a) What is noise data? How to handle noisy data. --- (5 Marks)
(b) Explain Regression. Write short note on Linear Regression. --- (5 Marks)
(c) Explain K – means clustering and solve the following with k=3 ----- (10 Marks)

{

2,

3,

6,

8,

9,

12,

15,

18,

22

}

6. (a) Using given training data set. Create classification model using decision tree and hence classify following table. --- (10 Marks)

Tid	Income	Age	Own House
1	Very High	Young	Yes
2	High	Medium	Yes
3	Low	Young	Rented
4	High	Medium	Yes
5	Very High	Medium	Yes
6	Medium	Young	Yes
7	High	Old	Yes
8	Medium	Medium	Rented
9	Low	Medium	Rented

(b) Suppose we have six object (within name A, B, C, D, E and F) and each object have two measured features (X1 and X2)

	X1	X2
A	1	1
B	1.5	1.5
C	5	5
D	3	4
E	4	4
F	3	3.5

Apply single linkage clustering and draw Dendrogram. --- (10 Marks)

7. Write notes on any two: --- (20 Marks)
(a) Applications of Web Mining
(b) Outlier analysis
(c) Market Basket Analysis and use of it
(d) Spatial Data Mining

Also see Data warehousing, Mining and business intelligence question papers for December 2010