M.Sc. (Semester IV) (CBCS) Examination Mar/Apr-2018
Computer Science
DATA MINING AND WAREHOUSE
Time: 2½ Hours
Max. Marks: 70
Instructions: Q.1 and Q.2 are compulsory. Attempt any three questions from Q. 3 to 7. Figures to the right indicate full marks.
Q.1

Choose the correct alternative:
14
KDD described the
whole process of extraction of knowledge from data
extraction of data
extraction of information
extraction or rules
Translation of problem to learning technique is called as
reengineering
translational engineering
representational engineering
learning algorithm
The partition of overall data warehouse is
database
data cube
data mart
operational data
OLAP stands for
Online Analytical Processing
Online Linear Analytical Processing
Online Animated Process
Online Analytical Problem
K-nearest neighbor is one of the
learning technique
OLAP tool
purest search technique
data warehousing tool
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
Which of the following is the not a types 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
B)State true or false
Data cleaning is process of adding noise and inconsistent data.
Information is collection of meaningful data.
Data mining is used to extract the data patterns.
Pattern recognition is not used to identify and classify the patterns.
04
Q.2
A)Write Short notes on
Data mart
Data reduction
08
B)Answer the following
What is data cube? Explain snowflake schema model in short.
What is noise? Explain binning method for smoothing the data
06
Q.3
Answer the following
a)What is mean by data warehouse? Explain the difference between OLTP and OLAP.
07
b)What is classification? Explain the issues regarding with classifications.
07
Q.4
Answer the following
a)Describe the functionalities of data mining
07
b)How to generate association rules from frequent item sets? Explain.
07
Q.5
Answer the following
a)Describe the data ware house architecture with well labeled diagram.
07
b)Explain various data mining primitives.
07
Q.6
Answer the following
a)Explain Agglomerative hierarchical clustering with example.
07
b)Explain Apriori algorithm with example.
07
Q.7
Answer the following
a)Define Data Mining. Explain their need and applications with examples.
07
b)Write an algorithm for k means for clustering.
07