PED – 132

*PED132*

II Semester M.E. (Information Technology) Degree Examination, January 2015 (2K13 Scheme) SE 23 : DATA MINING & WAREHOUSING (Common to SE/CSE/IT) Time : 3 Hours

Max. Marks : 100

Instruction : Answer any five full questions. 1. a) Define a data warehouse and discuss the different schemas for multidimensional data model.

10

b) Discuss the three-tier architecture of a data warehouse.

10

2. a) What is partial materialization ? Explain in brief.

5

b) Describe the following OLAP operations i) roll-up ii) drill-down iii) slice and dice and iv) Pivot.

8

c) Explain different types of OLAP servers.

7

3. a) What is Data Mining (DM) ? Explain the process of knowledge discovery in data bases.

8

b) Discuss numerocity reduction techniques for data reduction.

6

c) Explain the basic methods for Data cleaning.

6

4. a) Consider the transaction data set for an super market : Tid. List of Items

1

2

I1, I2 , I5

I2 , I4

3

4

I2 , I3 I1 , I2, I4

5

6

7

8

9

I1, I3

I2, I3

I1, I3

I1 , I2, I3 , I5

I1, I2, I3

Generate all the frequent itemsets using Apriori algorithm.

12

b) Explain multilevel association rules with examples.

8

5. a) Explain classification by decision tree induction with an example. Also list the characteristics of the decision tree induction.

8

b) What is back propagation ? Explain classification by back propagation.

8

c) Discuss the different ensemble techniques for increasing the accuracy of a classifier.

4 P.T.O.

*PED132*

PED – 132 6. a) Discuss the various data types in clustering. b) Explain the classification of various clustering algorithm. c) Discuss the OPTICS method of clustering.

6 4 10

7. a) How can the generalization be performed on set valued, list valued and sequence valued attributes ? Give examples.

10

b) Explain the Description based and content based retrieval for similarity searching in multimedia data.

10

8. Write short notes on :

20

i) Forms of coupling between data mining systems and data base/data warehouse systems. ii) Data mining applications iii) Spatial data mining iv) Text mining. ———————

DATA MINING & WAREHOUSING.pdf

iii) Spatial data mining. iv) Text mining. ———————. Page 2 of 2. DATA MINING & WAREHOUSING.pdf. DATA MINING & WAREHOUSING.pdf. Open. Extract.

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