DATA WAREHOUSING, MINING AND BUSINESS INTELLIGENCE (DWMBI), B.E. (IT) Semester- 7, (December 2013)

DATA WAREHOUSING, MINING AND BUSINESS INTELLIGENCE (DWMBI)

B.E. (IT) Semester- 7, (December 2013)

Con. 8517-13.

LJ-14017

(3 Hours)

[Total Mark 100]

N.B.: (1) Question No 1 is compulsory.

(2) Answer any four out of remaining six questions.

1. Answer any four: -

(a) Differentiate between DLAP and OLAP. --- (5 Marks)
(b) What is noisy data? How to handle it.--- (5 Marks)
(c) Explain constraint based association Rule mining. --- (5 Marks)
(d) Why is tree pruning useful in decision tree induction. --- (5 Marks)
(e) What is balanced score card. --- (5 Marks)

2. (a) Explain in details HITS algorithm in web mining. --- (10 Marks)
(b) What are issues regarding classification? Differentiate between classification and prediction. --- (10 Marks)

3. (a) Explain Data Mining Premitives. --- (10 Marks)
(b) Give the architecture of Typical Data Mining System. --- (10 Marks)

4. (a) Consider the following database with minimum support count = 60%. Find all frequent item set using Aprion and also generate strong association roles if minimum confidence = 50%.--- (10 Marks)

Tid	Item – brought
T1	{ M, O, N, K, E, Y }
T2	{ D, O, N, K, E, Y }
T3	{ M, A, K, E }
T4	{ M, U, C, K, E, Y }
T5	{ C, O, o, K, I, e }

(b) Explain multidimensional and multilevel association rules with an example. ---(10 Marks)

5. (a) What do you mean by pre-processing? Why it is required. --- (10 Marks)
(b) What is ETL process? Explain in detail giving emphasis on Data Transformation. --- (10 Marks)

6. (a) Explain Bayesian classification. --- (10 Marks)
(b) Explain periodic crawfer and Incremental Crawfer. --- (10 Marks)

7. Write short notes on any two: --- (20 Marks)
(a) Test Mining Approaches

(b) Numerority reduction

(c) Data Discretization and Sommarization.

Also see Data warehousing, Mining and business intelligence question papers for May 2013