iii) Spatial data mining. iv) Text mining. âââââââ. Page 2 of 2. DATA MINING & WAREHOUSING.pdf. DATA M
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 :
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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. ———————
