B.C.A. (Semester (CGPA) Examination, 2017
INTRODUCTION TO DATA MINING AND WAREHOUSING
Day Date: Wednesday, 19-04-2017 Max. Marks: 70
Time: 10.30 AM to 01.00 PM
N.B. All questions are compulsory.
Figures to the right indicate full marks.
Q.1 Choose correct alternatives: 14
predicts future trends, patterns behaviours, allowing top
level managers to make decisions.
Metadata Data Mining
Data Marts Data Warehouse
Star scheme consists of fact tables.
One Two Three Many
Data warehouse contain type of data which are not
available in operational environment.
Normalize Denormalize
Summarize Metadata
Noisy values can be handled by using technique.
Binning Regression
Clustering All of these
Data found in data warehouse is
Subject Oriented Time Variant
Integrated All of these
Total number of transactions supporting X in T is called as
Confidence Support Count
Association None of these
The long form of ETL is
Extraction Transformation and Loading
Extraction Tool for Loading
Extra Transformation and Loading
Estimated Transformation Loading
Two items are totally co related when correlation value is
Zero One
Greater than one Less than one
Page 1 of 2
SLR-U 25
of web mining uses the logs of web access.
Web Usage Web Content
Web Structure All of these
10) Parallel Processing speed up query processing, data loading and index
creation.
True False
11) Unstructured data such as text document can be mined using
technique.
Web Mining Unstructured Mining
Text Mining None of these
12) Association Rule mining is predictive data mining technique.
True False
13) Data with missing values are known as Inconsistent data.
True False
14) In data reduction, data compression must be lossless compression.
True False
Q.2 Answer the following:
Explain data visualization and parallel processing in detail. 07
Explain difference between operational data processing and analytical
data processing.
07
Q.3 Answer the following:
What is Web Mining? Explain Web Mining in detail. 07
Define data ware house and data mining. Explain metadata in detail. 07
Q.4 Attempt any one. 14
Explain different techniques used in data reduction and data
transformation.
Explain Data Mining Functionalities in detail.
Q.5 Attempt any one. 14
What are major issues in Data Mining? Explain each in detail.
What is support and confidence? Explain Apriori property. Explain
Apriori algorithm in detail. Find strong association from following table.
(Min_support=2 and min_confidence=70%)
Tr. ID
T1
T2
T3
T4
List of Items
D
E
E
E