Name of The Course
Course Code
Version No
Prerequisite
Co requisite
Anti- requisite
L
Data Mining and Data
Warehousing
3
BSCS3530
Date of Approval: 8/6/2021
Database Management System
T
P
C
0
0
3
Course Objectives:
1. To interpret the contribution of data warehousing and data mining to the decision support level
of organizations
2. To evaluate different models used for OLAP and data pre-processing
3. To categorize and carefully differentiate between situations for applying different data mining
techniques: mining frequent pattern, association, correlation, classification, prediction, and
cluster analysis
Course Outcomes
At the end of the course student will be able to:
CO1 Understand the functionality of the various data mining and data warehousing
component
CO2 Explain the analyzing techniques of various data.
CO3 Develop skill in selecting the appropriate data mining algorithm .
CO4 Master data mining techniques in various applications like social, scientific and
environmental context.
CO5 Compare different approaches of data ware housing and data mining with various
technologies.
CO6 Gain knowledge of latest trends and research areas in the course.
Text Book (s)
1. Data Mining- Concepts & Techniques; Jiawei Han &MichelineKamber- 2001, Morgan Kaufmann.
2. Data Warehousing In the Real World; Sam Anahory& Dennis Murray; 1997, Pearson
Reference Book (s)
1. Data Warehousing, Data Miniing and OLTP; Alex Berson, 1997, McGraw Hill.
2. Building the Data Warehouse; W.H. Inman, 1996, John Wiley & Sons.
3. Developing the Data Warehouses; W.H Ionhman,C.Klelly, John Wiley & Son
Unit-1
Introduction to Data Mining
8 hours
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.
Unit-2
Association Rule Mining
8 hours
Association Rule - Mining Frequent Patterns, Associations and Correlations – Mining
Methods – Mining various Kinds of Association Rules – Correlation Analysis – Constraint
Based Association Mining
Unit-3
Classification
8 hours
Classification and Prediction – Basic Concepts – Decision Tree Induction – Bayesian
Classification – Rule Based Classification – Classification by Back propagation – Support
Vector Machines – Associative Classification – Lazy Learners – Other Classification
Methods – Prediction.
Unit-4
Clustering
8 hours
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.
Unit-5
Introduction to Data Warehouse
8 hours
Introduction to Data Ware House, Differences between operational data base systems and
data Ware House, Data Ware House characteristics, Data Ware House Architecture and its
components, Extraction-Transformation-Loading, Logical (Multi Dimensional), Data
Modeling, Schema Design, star and snow-Flake Schema, Fact Constellation, Fact Table
Unit-6
4 hours
Advancement & Research
Advancement in the course: Real Time Data warehousing, Steam Mining , Research
methodologies, research discussion & publication
Continuous Assessment Pattern
Internal
(IA)
20
Assessment Mid
Term
(MTE)
30
Test End Term
(ETE)
50
Test Total Marks
100