M. Sc (Computer Science) (Sem IV) (CGPA) Examination, 2017
Data Mining and Warehouse
Day Date: Friday, 21-04-2017 Marks: 70
Time: 02.30 PM to 5.00 PM
Instruction Question no. 1 and 2 are compulsory.
Attempt any 3 questions from Q. no. 3 to Q. no. 7
Figures to the right indicate full marks.
Q.1 Choose correct alternatives. 10
is a subject oriented, integrated, time-variant,
nonvolatile collection of data in support of management
decisions.
Data mining Data warehousing
Web mining Text mining
describe the data contained in the data warehouse.
Relational data Operational data
Metadata Informational data
The granularity of the fact is the of detail at which it is
recorded.
Transformation Summarization
Level Transformation and Summarization
Cluster is
Group of similar objects that differ significantly from other
objects.
Operations on database to transform or simplify data in
order to prepare it for a machine learning algorithm.
Symbolic representation of facts or ideas from which
information can potentially be extracted.
None of these
An OLTP system is oriented.
Market Business management Customer
The cuboid that holds the lowest level of summarization is
called the
Apex cuboid Base cuboid
3-D cuboid 2-D cuboid
Fact constellation is also called a schema.
Galaxy snowflake star none of these
0
to the level of understanding and insight that
is provided by the classifier or predictor.
Interpretability Scalability
Robustness None of these
aggregation operations are applied to the data
Page 2 of 2
in the construction of a data cube.
Dimensionally reduction Attribute subset selection
Data cube aggregation Numerosity reduction
10) The full form of OLAP is
Online Advanced Processing
Online Analytical Processing
Online Advanced Preparation
Online Analytical Performance
State True or False. 04
OLAP system mainly focuses on the current data.
Task-relevant data specifies the portions of the database or
The set of data in which the user is interested.
Retail data mining can help identify customer buying
behavior, discover customer shopping patterns and trends,
improve the quality of customer service.
An enterprises warehouse collects all of the information about
subjects spanning the entire organization.
Q.2 Write short notes of the following. 08
Data Transformation
Trends in Data Mining
Answer the following. 06
What is Data Cleaning? Explain any two methods for missing
Values.
Explain Virtual warehouse.
Q.3 Answer the following. 14
What is Data Warehouse? Explain the difference between OLTP
and OLAP.
What is data cube? Explain Star schema and Snowflake
schema with diagram.
Q.4 Answer the following. 14
What is classification? Explain Issues regarding Classification
and Prediction.
Explain the steps for Bayesian classification.
Q.5 Answer the following. 14
Explain Data Mining primitives.
What is Association Rule? Explain multilevel association rule
from transaction database.
Q.6 Answer the following. 14
Discuss market basket analysis in detail.
What is Cluster Analysis? Explain typical requirements of
clustering in data mining.
Q.7 Answer the following. 14
Explain various data mining applications in detail.
Explain the procedure for K-means algorithm.