Download Detailed Syllabus

Detailed Syllabus
Lecture-wise Breakup
Subject
Code
16B1NCI438
Semester
Even
Semester
17
Fourth
Month
from January
Subject
Name
Introduction Data warehouse and Data mining
Credits
3
Coordinator
Mr. Avinash Pandey
Contact Hours
Session 2016-
3
The objectives of this course are:

Learning
Objective
Learning
Outcome
Identify the scope and necessity of Data Mining & Warehousing for the
society.
 Describe the designing of Data Warehousing so that it can be able to solve the
root problems.
 To understand various tools of Data Mining and their techniques to solve the
real time problems.
 To develop ability to design various algorithms based on data mining tools.
 To develop further interest in research and design of new Data Mining
techniques.
Upon successful completion of this course, student should:
 Able to understand the functionality of the various data mining and data
warehousing components
 Apply the techniques of clustering, classification, association finding, feature
selection and visualisation on real world data.
 Determine whether a real world problem has a data mining solution
JIIT University, Noida
Module No.
1.
2
3.
4.
5.
Subtitle of the
Module
Topics in the module
Introduction to data
ware house
Data
warehousing
components,
data
extraction, cleanup, and transformation tools
–metadata; business analysis - reporting and
query tools and applications, online
analytical
processing
(OLAP),
multidimensional data model;
Data Mining
Introduction, types of data, data mining
functionalities, interestingness of patterns,
integration of a data mining system with a
data warehouse , issues , role of data preprocessing and data normalization;
Association rule
mining and
classification
Cluster Analysis
Applications and
Trends in Data
Mining
Mining Frequent Patterns, Associations and
Correlations – Mining Methods – Mining
Various Kinds of Association Rules
Classification and Prediction - Basic
Concepts, Decision Tree Induction, Bayesian
Classification, Support Vector Machines,
Other Classification Methods
Types of Data in Cluster Analysis, A
Categorization of Major Clustering Methods,
Partitioning Methods, Hierarchical Methods,
Density-Based
Methods,
Grid-Based
Methods, Model-Based Clustering Methods,
Clustering High-Dimensional Data
Data Mining Applications: Social Network
Analysis, Mining Sequence Patterns in
Biological Data, Text Mining
Total number of Lectures
JIIT University, Noida
No. of
Lectures for
the module
8
8
10
10
4
40
Recommended Reading material: Author(s), Title, Edition, Publisher, Year of Publication
etc. ( Text books, Reference Books, Journals, Reports, Websites etc.)
1.
W. H. Inmon, "Building the Data Warehouse", 3rd edition
2.
Anahory and Murray, Data warehousing in the real world, Pearson
education/Addison Wesley.
3.
Margaret Dunham, Data Mining: Introductory and Advanced Topics,
Published by Prentice Hall.
4.
Jiawei Han, Micheline Kamber, "Data Mining: Concepts and Techniques",
Morgan Kaufmann Publishers, 2002. (www.cs.sfu.ca/~han/DMbook.html).
5.
George M Marakas, Modern Data Warehousing , Mining and Visualization-,
Peason Education
Evaluation Scheme
T1
1.
T2
2.
T3
3.
Mini projects
4.
+ Attendance
Total
20 Marks
20 Marks
35 Marks
25 Marks
100 Marks
JIIT University, Noida