M.Sc. (Semester IV) (CBCS) Examination Oct/Nov-2017
Computer Science
DATA MINING AND WAREHOUSE
Day Date: Monday, 20-11-2017 Max. Marks: 70
Time: 02.30 PM to 05.00 PM
Instructions: 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
KDD describes the
Whole process of extraction of knowledge from data
Extraction of data
Extraction of information
Extraction of 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
The decision support system is used only for
Cleaning Coding
Selecting Queries
The complexity of data mining algorithm is represented by
log n. 2n log n.
n log n. 2 log n.
The set of attribute in a database that refers to data in another table is
called
Primary key Candidate key
Foreign key Super key
In KDD and data mining, noise is referred to as
Repeated data Complex data
Meta data Random errors in database
Page 2 of 2
SLR-MG-314
10) Data mining algorithm require
Efficient sampling method.
Storage of intermediate results.
Capacity to handle large amounts of data
All of the above
State True or False. 04
Data bout data is called as meta data.
Data cleaning is process of removing noise and inconsistent data.
Information is collection of meaningful data.
Data is collection of information.
Q.2 Write short notes of the following. 08
i. Data cleaning
ii. Star Schema
Answer the following:- 06
I. State and explain Issues regarding classifications.
II. What do you meant by cluster analysis? Explain in short.
Q.3 Answer the following.
Describe the functionalities of data mining. 07
Explain the multilevel associations rules from transactional database. 07
Q.4 Answer the following.
State and Explain the steps in decision tree induction method. 07
Write the algorithm for k-means for clustering. 07
Q.5 Answer the following.
Describe the architecture of Data warehouse with well labelled diagram. 07
Explain various data mining applications. 07
Q.6 Answer the following.
Explain Apriori algorithm with example. 07
State and explain data mining primitives. 07
Q.7 Answer the following.
Explain OLAP Services with example. 07
Discuss the features of data mining query language. 07