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MU Computer Engineering (Semester 8)
Data Warehouse & Mining
May 2017
Total marks: --
Total time: --
INSTRUCTIONS
(1) Assume appropriate data and state your reasons
(2) Marks are given to the right of every question
(3) Draw neat diagrams wherever necessary

1(A) What is dimensional modelling ? Design the data warehouse for wholesale furniture Company. The data warehouse has to allow analysing the company's situation at least with respect to the Furniture, Customer and Time. More ever, the company customer needs to analyse: The furniture with respect to its type category and material. The customers with respect to their spatial location, by considering at least cities, regions and states. The company is interested is learning the quantity, income and discount of its sales.
10 M
1(A) What is dimensional modeling? Design the data warehouse for wholesale furniture Company. The data warehouse has to allow analysing the company's situation at least with respect to the Furniture , Customer and Time. More ever, the company needs to analyse: The furniture with repect to its type category and material. The cutomer with respect to their spatial location, by considering at least citites, regions and states. The company is interested in learning the quantity, income and discount of its sales.
10 M
1(B) Discuss different steps involved in Data Pre-processing
10 M
1(B) Discuss the different steps involved in Data Pre-processing.
10 M

2(A) The college wants to record the Marks for the courses completed by students using the dimesions: i) Course , ii) Student, iii) Time & measure Aggregate marks Create a cube describe following OLAP operations.
i) Slice ii) Dice iii) Roll up iv) Drill down v) Pivot
10 M
2(A) The college wants to record the Marks fot 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
10 M
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 low yes fair yes 6 old low yes good no 7 middle low yes good yes 8 young medium no fair no 9 young low yes fair yes 10 old medium yes fair yes 11 young medium yes good yes 12 middle medium no good yes 13 middle high yes fair yes 14 old medium no good no
10 M
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
10 M

3(A) Explain ETL of data warehousing in details?
10 M
3(A) Explain ETL of data warehousing in details?
10 M
3(B) Explain types of attributes and data visualization for data exploration
10 M
3(B) Explain types of attributes and data visualization for data exploration.
10 M

4(A) Illustrate the architecture of Data Warehouse system. Differentiate Data warehouse and Data Mart.
10 M
4(A) Illustrate the architecture of Data Warehouse system. Differentiate Data warehouse and Data Mart.
10 M
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}
10 M
4(B) Explain K- Means clustering algorithm? Apply K-mean algortihm for the following Data set with two clusters. Data Set= {15, 15, 16, 19,19, 20, 20, 21, 22, 28, 35,40, 41, 42, 43, 44, 69, 61,65}
10 M

5(A) Explain Updates to dimension tables in detail.
10 M
5(A) Explain Updates to dimensions table in detail.
10 M
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
10 M
5(B) A database has ten transactions. Let minimum support = 30% and minimum.
Confidence =70%
i) Find all frequent patterns using Apriori Algorithm.
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 10 B,C,D,F
10 M

Write short note any four question from Q.6(a, b, c, d, e,)
6(a) Major-issues in Data Mining
5 M
Write short note any four question from Q.6(a, b, c, d, e)
6(a) Major issues in Data Mining
5 M
5 M
5 M
6(c) FP Tree
5 M
6(c) FP Tree
5 M
6(d) DBSCAN
5 M
6(d) DBSCAN
5 M
6(e) Hierarchical Clustering
5 M
6(e) Hirerarchical Clustering
5 M

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