Download Course Title Data Warehousing and Data Mining Course Code

Course Title
Data Warehousing and Data Mining
Course Code
CP108
Theory
:03
Practical
:01
Tutorial
:00
Credits
:04
Course Credit
Course Objective
At the end of the course, students will be able to:
Understand the basic concepts of data warehousing.
Analyze the major techniques of preprocessing for different types of data.
Understand and Compare different types of data warehouse architecture.
 Recognize the different type of data mining methods.
 Differentiate the technique of data mining and Demonstrate that technique
with implementing them.
 Investigate modern Trends and technique of data mining.
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Detailed Syllabus
Sr.
No.
1
2
Name of chapter & details
Section – I
Introduction to Data Warehousing
What is data warehousing - The building Blocks: Defining Features –
Data warehouses and data marts - Overview of the components Metadata in the data warehouse - Need for data warehousing
Preprocessing Techniques
Overview, Need for pre-processing, Issues related to efficient data
handling (Extraction, Transformation, And updating of large databases
(ADDED), Data Cleaning, Data Integration & Transformation, Data
Reduction, Discretization & Concept Hierarchy Generation
Hour
s
Allott
ed
04
05
3
4
Business analysis
Multi-dimensional Data Cubes, Star, Snow Flakes, & Fact Constellation
Schema, Concept Hierarchies, OLAP, Data Warehouse Architecture
Steps for design and construction of data warehouse, A 3-tier data
warehouse architecture, ROLAP, MOLAP, HOLAP, Data Warehouse
Implementation
Implementation and Maintenance
Physical design process, data warehouse deployment, growth and
maintenance.
06
06
Section – II
5
6
7
8
9
Introduction to DATA MINING
Introduction, Data, Types of Data, Data Mining Functionalities,
Interestingness of Patterns, Classification of Data Mining Systems,
Data Mining Task Primitives, Integration of a Data Mining System with
a Data Warehouse Issues, Data Preprocessing
Concept Description and Association Rule Mining
What is concept description?, Data Generalization and summarization
based characterization, Attribute relevance, class comparisons
Association Rule Mining: Market basket analysis basic concepts,
Finding frequent item sets: Apriori algorithm, generating rules,
Improved Apriori algorithm
04
Classification and prediction
What is classification and prediction?, Issues regarding Classification
andprediction: Classification methods: Decision tree, Bayesian
Classification, Rule based, CART, Neural Network, CBR, Rough set
Approach, Fuzzy Logic, Genetic AlgorithmsPrediction methods: Linear
and non linear regression, Logistic Regression
Clustering and applications and trends in data mining
Cluster Analysis, Types of Data, Categorization of Major Clustering
Methods Kmeans, Partitioning Methods, Hierarchical Methods,
Density-Based Methods, Grid Based Methods, Model-Based Clustering
Methods, Clustering High Dimensional Data, Constraint– Based Cluster
Analysis, Outlier Analysis, Data Mining Applications.
04
Application and Trends in Data Mining
Applications, Systems products and research prototypes, Additional
themes in data mining, Trends in data mining, Mining Time-Series and
Sequence Data – Mining Text Databases – Mining the World Wide
Web – Data Mining Application – Web mining
03
04
06
Instructional Method and Pedagogy
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Lectures will be conducted with the aid of multi-media projector, blackboard,
OHP etc. Assignments based on course contents will be given to the students
at the end of each unit/topic and will be evaluated at regular interval
Minimum five experiments shall be there in the laboratory related to course
contents
Minimum ten tutorials which includes solution of minimum their case studies in
each head
Reference Books
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J. Han, M. Kamber, Data Mining Concepts and Techniques, Morgan
Kaufmann Publishers
ReemaThareja, Data Warehousing, Oxford
Paulraj Ponnian, Data Warehousing Fundamentals,John Willey
Alex Berson and Stephen J. Smith, Data Warehousing, Data Mining
& OLAP, Tata McGraw – Hill
Additional Resources
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http://cquestionbank.blogspot.com
www.intelligentedu.com/
www.hermetic.ch/cfunlib.htm
N.P.T.L. Video Lecture Series
N.I.T.T.I. Instructional Resources Videos.
www.cprogramming.com/
www.c-program.com/