Data Warehouse & Mining : Question Paper Dec 16 - Computer Engineering (Semester 8) | Mumbai University (MU)

Data Warehouse And Mining - Dec 16

Computer Engineering (Semester 6)

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

1.a Consider following dimensions for a Hypermarket chain: Product, Store, Time and Promotion. With respect to this business scenario, answer the following questions, Clearly state any reasonable assumptions you make- Design a star schema, Whether the star schema can be converted to snowflake schema? Justify your answer and draw snowflake schema for the data warehouse (clearly mention the Fact table(s), Dimension table(s), their attributes and measures).
(10 marks) 12778

1.b Define linear, non-linear and multiple regressions. Plan a regression model for Disease development with respect to change in weather parameter
(10 marks) 12779

2.a What is meant by metadata in the context of a Data warehouse? Explain the different types of meta data stored in a data warehouse. it with a suitable example.
(10 marks) 5392

2.b Describe the various functionalities of Data mining as a step in the process of knowledge Discovery.
(10 marks) 5925

3.a In what way ETL cycle can be used in typical data ware house. explain with suitable instance
(10 marks) 5912

3.b What is Clustering Technique ? Discuss the Agglomerative algorithm with the following data and plot a Dendrogram using single link approach. The table below comprises sample data items indicting the distance between the elements.

Item E A C B D
E 0 1 2 2 3
A 1 0 2 5 3
C 2 2 0 1 6
B 2 5 1 0 3
D 3 3 6 3 0

(10 marks) 12780

4.a Discuss how computations can be performed efficiently on data cubes.
(10 marks) 5919

4.b A database has five transactions. Let min-support = 60_ and min- confidence = 80%. Find all frequent item sets by using Apriori Algorithm T_ID is the transaction ID

T_ID Items bought
T-1000 M,O,N,K,E,Y
T-1001 D,O,N,K,E,Y
T-1002 M,A,K,E
T-1003 M,U,C,K,Y
T-1004 C,O,O,K,E

(10 marks) 12781

5.a.i Differentiate : OLTP VS OLAP
(5 marks) 5915

5.a.iI Differentiate : Date Warehouse VS Data Mart
(5 marks) 5391

5.b Why naïve Bayesian classification is called “naive”. Briefly outline the major ideas of naïve Bayesian classification
(10 marks) 12782

6.a Write Short Note On : Application of Data Mining to Financial Analysis
(5 marks) 5924

6.b Write Short Note On : Fact less Fact Table
(5 marks) 5966

6.c Write Short Note On : Index OLAP data
(5 marks) 5918

6.d Write Short Note On : Data Quality
(5 marks) 5914

6.d Write Short Note On : Decision Tree based Classification Approach
(5 marks) 12783


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