Data Warehousing, Mining and Business Intelligence : Question Paper Dec 2013 - Information Technology (Semester 7)

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Data Warehousing, Mining and Business Intelligence - Dec 2013

Information Technology (Semester 7)

TOTAL MARKS: 100
TOTAL TIME: 3 HOURS (1) Question 1 is compulsory.
(2) Attempt any four from the remaining questions.
(3) Assume data wherever required.
(4) Figures to the right indicate full marks.

Attempt any four :-

1 (a) Differentiate between OLAP and OLTP(5 marks) 1 (b) What is noisy data? How to handle it.(5 marks) 1 (c) Explain constraint based association rule mining.(5 marks) 1 (d) Why is tree pruning useful in decision tree induction.(5 marks) 1 (e) What is balanced score card.(5 marks) 2 (a) Explain in details HITS algorithm in web mining.(10 marks) 2 (b) What are issue regarding classification? Different between classification and prediction.(10 marks) 3 (a) Explain Data Mining Premitives.(10 marks) 3 (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 Appriori and also generate strong association rules if minimum confidence =50%.

TID	Items-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,Y}
T5	{C,O,O,K,I,E}

(10 marks) 4 (b) Explain multidimensional and multilevel association rules with an example.(10 marks) 5 (a) What do you mean by preprocessing? Why it is required.(10 marks) 5 (b) What is ELT process? Explain in detail giving emphasis on Data Transformation.(10 marks) 6 (a) Explain Bayesian classification.(10 marks) 6 (b) Explain periodic crawler and Incremental crawler.(10 marks)

Write short notes any two :-

7 (a) Test Mining Approaches(10 marks) 7 (b) Numerority reduction.(10 marks) 7 (c) Data Discretization and Summarization.(10 marks)

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