1(A) What is dimensional modeling? Design the data warehouse for wholesale furniture Company. The data warehouse has to allow analyzing the company's situation at least with respect to the Furniture , Customer and Time. More ever, the company needs to analyses: The furniture with respect to its type category and material. The customer with respect to their spatial location, by considering at least cities, regions and states. The company is interested in learning the quantity, income and discount of its sales.

1(B) Discuss the different steps involved in Data Pre-processing.

2(A) The college wants to record the Marks for the courses completed by students using the dimensions : i) Course, ii) Student, iii) Time & measure Aggregate marks Create a Cube and describe following OLAP operations.
i)Slice ii) Dice iii) Roll up iv) Drill down v) Pivot

2(B) Apply the Naive Bayes classifier algorithm for buys computer classification and classify the tuple = X(age "young". Income = "medium", student = "yes" and credit - rating = "fair")

ID	Age	Income	Student	Credit-rating	buys computer
1	young	high	no	fair	no
2	young	high	no	good	no
3	middle	high	no	fair	yes
4	old	medium	no	fair	yes
5	old	medium	no	fair	yes
6	old	low	yes	good	no
7	middle	low	yes	good	yes
8	young	medium	no	fair	yes
9	young	low	yes	fair	yes
10	old	medium	yes	fair	yes
11	young	medium	yes	fair	yes
12	middle	medium	no	good	yes
13	middle	high	yes	fair	yes
14	old	medium	no	good	no

3(A) Explain ETL of data warehousing in details?

3(B) Explain types of attributes and data visualization for data exploration

4(A) Illustrate the architecture of Data Warehouse system. Differentiate Data warehouse and Data Mart.

4(B) Explain K-means clustering algorithm? Apply K-Means algorithm for the following Data Set = { 15, 15, 16, 19, 20, 21, 22, 28, 35, 40, 41, 42, 43, 44, 60, 61, 65}

5(A) Explain Updates to dimensions table in detail.

5(B) A database has ten transactions. Let minmum support = 30% and minimum Cofidence = 70%
i) Find all frequent patterns using AprioriAlgorithm.
ii) List strong association rules.

Transaction_Id	Items
01	A, B, C, D
02	A, B, C, D, E, G
03	A, C, G, H, K
04	B,C, D, E, K
05	D, E, F, H, L
06	A, B, C, D, L
07	B, I, E, K, L
08	A, B, D, E, K
09	A, E, F, H, L
010	B, C, D, F

6(a) Major-issues in Data Mining

6(b) Metadata in Data Warehouse

6(c) FP Tree

6(d) DBSCAN

6(e) Hierarchical Clustering