Data Warehousing, Mining and Business Intelligence - Dec 2011

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. 1(a) A manufacturing company has a huge sales network. To control the sales, it is divided into regions. Each region has multiple zones. Each zone has 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 data warehouse schema to take into consideration of above granularity levels for regions, sales person and quarterly, yearly and monthly sales.(10 marks) 1(b) Compare database and data warehouse.(5 marks) 1(c) Explain Business Intelligence issues.(5 marks) 2(a) What are major issues in Data Mining (5 marks) 2(b) Explain BIRCH method of clustering with an example.(5 marks) 2(c) Explain Data integration and Transformation with an example. (10 marks) 3(a) Explain techniques of Web Structure Mining.(10 marks) 3(b) Explain KDD process in detail.(10 marks) 4(a) How the FP tree is better than Apriori algorithm.(5 marks) 4(b) A database has four transactions. Let minimum support and confidence be 50%. D=

(5 marks) 4(c) Explain constraint based association rule mining.(5 marks) 4(d) Explain multilevel association rules.(5 marks) 5(a) What is noisy data. How to handle it?(5 marks)

Write short notes on any two :-

5(b) Explain Regression? Write short note on Linear Regression.(5 marks) 5(c) Explain K-means clustering and solve the following with k=3{2, 3, 6, 8, 9, 15, 18, 22}(10 marks) 6(a) Using given training data set create classification model using decision tree and hence classify following tuples.

(10 marks) 6(b) Suppose we have six objects (with name A, B, C, D, E, F) and each object have two measured features (X1 and X2)

Apply single linkage clustering and draw dendrogram.(10 marks)

Write Notes on (Any Two)

7(a) Applications of Web Mining.(10 marks) 7(b) Outlier analysis.(10 marks) 7(c) Market Basket Analysis and use of it. (10 marks) 7(d) Spatial Data Mining.(10 marks)