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Data Warehouse And Mining Question Paper - Dec 17 - Computer Engineering (Semester 8) - Mumbai University (MU)
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Data Warehouse And Mining - Dec 17

Computer Engineering (Semester 8)

Total marks: 80
Total time: 3 Hours
INSTRUCTIONS
(1) Question 1 is compulsory.
(2) Attempt any three from the remaining questions.
(3) Draw neat diagrams wherever necessary.

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. Each sales person is allocated different cities. The objective is to track sales figure at different granularity levels of region and to count no. of products sold. Design a star schema by considering granularity levels for region, sales person and time. Convert the star schema to snowflake schema.
(10 marks) 12790

1.b. Discuss:

i) Architecture of a typical data mining system.

ii) Application and major issues in Data Mining

(10 marks) 12791

2.a. Consider a data warehouse for a hospital where there are three dimensions:

a) Doctor b) Patient c) Time

Consider two measures

i) Count

ii) Charge where charge is the fee that the doctor charges a patient for a visit.

For the above example create a cube and illustrate the following OLAP operations.

1) Rollup 2 ) Drill down 3) Slice 4) Dice 5) Pivot.

(10 marks) 12792

2.b. Consider the data given below. Create adjacency matrix. Apply single link algorithm to cluster the given data set and draw the dendogram.

Object Attribute 1 (X) Attribute 2 (Y)
A 2 2
B 3 2
C 1 1
D 3 1
E 1.5 1.5

(10 marks) 12793

3.a. Define Metadata. Discuss the types of Metadata stored in a data warehouse. Illustrate with an example.
(10 marks) 5392

3.b. Discuss different steps involved in Data Pre-processing.
(10 marks) 5927

4.a. Discuss various OLAP Models and their architecture.
(10 marks) 12794

4.b. Define Classification. Discuss the issues in Classification. A simple example from the stock market involving only discrete ranges has profit as categorical attribute, with values {Up, Down) and the training data is:

Age Competition Type Profit
Old Yes Software Down
Old No Software Down
Old No Hardware Down
Mid Yes Software Down
Mid Yes Hardware Down
Mid No Hardware Up
Mid No Software Up
New Yes Software Up
New No Hardware Up
New No Software Up

Apply decision tree algorithm and show the generated rules.

(10 marks) 12795

5.a. Differentiate top-down and bottom-up approaches for building data warehouse. Discuss the merits and limitations of each approach.
(10 marks) 12796

5.b.i. Discuss Association Rule Mining and Apriori Algorithm.
(10 marks) 12797

5.b.ii. A database has four transactions. Let minimum support = 50% and minimum confidence = 50%

Transaction_Id Items bought
T100 A, B, C
T200 A, C
T300 A, D
T400 B, E, F

Find all frequent item sets using apriori algorithm. List strong association rules.

(5 marks) 12798

Q.6 Write short note on the following (Answer any FOUR)

6.a. Fact Constellation
(5 marks) 12799

6.b. Data visualization
(5 marks) 12787

6.c. FP Tree
(5 marks) 5965

6.d. DBSCAN
(5 marks) 5942

6.e. ETL Process
(5 marks) 5912

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