Reg. No. :

M.C.A. DEGREE EXAMINATION, FEBRUARY/MARCH 2018.

Third Semester

DMC 7302 — DATA WAREHOUSING AND MINING

(Regulations 2013)

Time : Three hours Maximum : 100 marks

Answer ALL questions.

PART A — (10 2 = 20 marks)

1. What is a data mart?

2. Give an account on drill-down operation.

3. What is a multi dimensional database?

4. What is an apex cuboid?

5. State the need for data cleaning.

6. What is evolution analysis?

7. What is correlation analysis?

8. What is rule based classification? Give an example.

9. How to evaluate accuracy of a classifier?

10. What is an outlier? Mention its applications.

PART B — (5 13 = 65 marks)

11. (a) With the neat sketch explain architecture of data warehouse. (13)

Or

(b) (i) Explain Indexing (5)

(ii) Explain OLAP operations. (8)

Question Paper Code : J1363

J1363 2

12. (a) Explain data cleaning with example. (13)

Or

(b) Write the need for data preprocessing. (13)

13. (a) Write Apriori algorithm for finding frequent item sets and explain. (13)

Or

(b) Apply a priori algorithm to the following data set. State and discuss each step in the Apriori algorithm. Assume the transaction. (13)

Trans ID Items Purchased

101 Apple, Orange, Litchi, Grapes

102 Apple, Mango

103 Mango, Grapes, Apple

104 Apple, Orange Litchi, Grapes

105 Pears, Litchi

106 Pears

107 Pears, Mango

108 Apple Orange, Strawberry, Litchi, Grapes

109 Strawberry, Grapes

110 Apple, Orange, Grapes

The set of items is {Apple, Orange, Strawberry, Litchi, Grapes, Pears, Mango}. Use 0.3 for the minimum support value.

14. (a) What is classification? With an example explain how support vector machines can be used for classification. (3+10)

Or

(b) (i) Explain the algorithm for constructing a decision tree from training samples. (7)

(ii) Mention about advantage and disadvantage of decision tree over other classification techniques. (6)

15. (a) What is grid based clustering? With an example explain an algorithm for grid based clustering. (3+10)

Or

(b) (i) Give an account on the requirements of clustering algorithms. (6)

(ii) Compare K-means and K-medoid algorithms. (7)

PART C — (1 15 = 15 marks)

16. (a) Explain density based local outlier detection. (15)

Or

(b) List and explain the classification of various clustering methods in data mining. (15)

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Reg. No. :

M.C.A. DEGREE EXAMINATION, AUGUST/SEPTEMBER 2017.

Third Semester

DMC 7302 — DATA WAREHOUSING AND MINING

(Regulations 2013)

Time : Three hours Maximum : 100 marks

Answer ALL questions.

PART A — (10 2 = 20 marks)

1. Give some benefits in Data Ware housing.

2. Write short notes of Indexing.

3. Why needs in Data Preprocessing?

4. Write out Data Reduction.

5. What is Association Rule Mining?

6. How to generate association rules from frequent item sets?

7. Define data Prediction.

8. What is meant by SVM?

9. What is clustering?

10. What is Grid and it methods?

PART B — (5 13 = 65 marks)

11. (a) Describe in detail about Multidimensional Data Model. (13)

Or

(b) Explain OLAP operations with suitable Examples. (13)

Question Paper Code : BS2363

BS2363 2

12. (a) Discuss about the KDD process with suitable example. (13)

Or

(b) Briefly explain of Data Integration and Transformation. (13)

13. (a) Describe in detail about Mining Frequent item sets with Candidate

Generation. (13)

Or

(b) Write about Constraint-Based Association Mining. (13)

14. (a) Write short notes on (i) Bayesian Classification, (ii) Rule Based

Classification. (13)

Or

(b) Explain the following with Examples: (i) Classification by Decision Tree

(ii) Classification by Back propagation. (13)

15. (a) Describe in detail about partitioning Methods. (13)

Or

(b) Explain detail about the Hierarchical methods with suitable Examples.

(13)

PART C — (1 15 = 15 marks)

16. (a) Case study: Customer response prediction and profit optimization in

Data mining. (15)

Or

(b) Write a case study: Data Ware housing for a health management system.

(15)

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