Data Warehousing, Mining and Business Intelligence : Question Paper May 2014 - Information Technology (Semester 7)

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Data Warehousing, Mining and Business Intelligence - May 2014

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

1 (a) What are major issue in data mining?(5 marks) 1 (b) Explain different OLAP operations.(5 marks) 1 (c) Difference between database and data warehouse.(5 marks) 1 (d) Write a short note on Linear regression.(5 marks) 2 (a) Explain constraint based and multilevel association rules with an example.(10 marks) 2 (b) Explain market basket analysis and uses of it.(10 marks) 3 (a) Explain BRICH method of clustering with an example.(10 marks) 3 (b) Explain Regression. Write short note on Non-Linear regression.(10 marks) 4 (a) Explain data cleaning, data transformation and Integration with an example.(10 marks) 4 (b) Apply Bayesin classification to predict class of new tuple (Nicol, Female, 1.67m). Use the following data.

Person ID	Name	Gender	Height	Class
1	Kristina	Female	1.6 m	Short
2	Jim	Male	2 m	Tall
3	Maggie	Female	1.9 m	Medium
4	Martha	Female	1.85 m	Medium
5	John	Male	2.8 m	Tall
6	Bob	Male	1.7 m	Short
7	Clinton	Male	1.8 m	Medium
8	Nyssa	Female	1.6 m	Short
9	Kathy	Female	1.65 m	Short

(10 marks) 5 (a) What are outlier. Explain outlier analysis.(10 marks) 5 (b) Explain K-means clustering and solve the following with k=3
{2,3,6,8,9,12,15,18,22}(10 marks) 6 (a) Explain Bussiness Intelligence issues.(10 marks) 6 (b) Describe the steps involved in data mining when viewed as a process of Knowledge discovery.(10 marks)

Short note on any three

7 (a) Application of Web Mining(7 marks) 7 (b) Market segmentation(7 marks) 7 (c) Sequence Mining in Transaction(7 marks) 7 (d) Agglomerative clustering.(7 marks)

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