M.Sc. (Computer Science) (Sem IV) (CBCS) 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
Query tools and data mining tools are
Same different complementary standard
In K nearest neighbour the input is translated to
Values
Points in multidimensional space
Strings of characters
Nodes
is a mining tool from integral solutions.
WEKA web miner rapid miner clementine
The is one of the operation research techniques.
Associate rules K-nearest neighbour
Decision tress Genetic algorithm
Metadata is used by the end users
Managing database Structuring database
Querying purposes Making decisions
SQL stands for
Simple query language
Structured query language
Strong query language
Simple language
Association rules are always defined on
Binary attribute
Single attribute
Relational database
Multidimensional attribute
analysis divides data into groups that are
meaningful, useful, or both.
Cluster Association Classification Relation
Page 2 of 2
Which of the following is the not a type of clustering?
K-means Hierarchical Partitional Splitting
10) The goal of data mining is
To explain some observed event or condition
To confirm that data exists.
To analyze data for expected relationships.
To create a new data warehouse.
State True or False. 04
The operational data are used as a source for the data
Warehouse.
Data Integration is not the process of combining multiple data
Sources.
Data mining is used to extract the data patterns.
Pattern recognition is used to identify and classify the
patterns.
Q.2 Write short notes of the following. 08
Trends in data mining
Data Transformation
Answer the following. 06
What is data Cleaning? Explain it.
State and Explain in short issues regarding with
classifications.
Q.3 Answer the following.
Describe the various data mining primitives. 07
Explain the multilevel association rules from transactional
databases.
07
Q.4 Answer the following.
Explain the procedure in Bayesian classification method with
example.
07
What is data cube? Explain different forms of multidimensional
data model.
07
Q.5 Answer the following.
Define data warehouse? Explain the difference between OLTP
and OLAP.
07
What is clustering? Explain Agglomerative and divisive
hierarchical clustering.
07
Q.6 Answer the following.
Explain OLAP operations with example. 07
Define Data Mining. Explain their need and applications with
examples.
07
Q.7 Answer the following.
How to extract the rules from a decision tree? Explain. 07
Explain K-means algorithm in detail. 07