Download Course Title Data Warehousing and Data Mining

Course Title
Data Warehousing and Data Mining
Course Code
BCA615
Course Credit
Theory(Hrs)
:3
Practical(Hrs)
:0
Tutorial(Hrs)
:0
Credits
:3
Course Objectives
The objectives of the course are:
 To comprehend the architecture of a Data Warehouse and the need for preprocessing
 To understand the concept of Analytical Processing (OLAP) and Transaction
Processing (OLTP)
 To understand the need for Data Mining and advantages to the business world
 To identify the different applications of Data Mining
 To learn the algorithms used for various type of Data Mining problems
Detailed Syllabus
Sr.
No.
Name of chapter & details
Hours
Allotted
Section – I
1
Introduction to Data Warehouse
Definition, Data Warehouse Keywords, Differences between
Operational Database Systems and Data Warehouses; Difference
between OLTP & OLAP, Overview of Multi-dimensional Data Model,
Basic steps to develop data warehouse architecture, Data warehouse
system architecture (Two-Tiered and Three-Tiered) Data Warehouse
Implementation, Data Cube Technology, From Data warehousing to
Data Mining, Introduction to Data Cube: OLAP Operations in Multidimensional Data Model: Roll-up, Drill-down, Slice & Dice, Pivot
08
BCA, School of Computer Science, RK University
(Rotate), Types of OLAP : ROLAP versus MOLAP versus HOLAP
2
Data Marts
Data Marts: Data Mart structure, Usage of Data Mart, Data warehouse
and Data Mart
04
3
Pre-processing
Pre-Processing: Data Cleaning, Data Integration and Transformation,
Data Reduction, Discretization and concept Hierarchy Generation,
ETL Process : Extraction of Data, Transformation of Data, Loading of
Data, Comparison
05
Section – II
4
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 with Data Mining, KDD and Business
Intelligence
06
5
Association Rule Mining
Basic Concepts: Market Basket Analysis; Frequent Itemsets, Closed
Itemsets,
Association Rules: Frequent Pattern Mining, Apriori Algorithm: Finding
Frequent Itemsets using Candidate Generation; Generating, Association
Rules from Frequent Itemsets; Improving the Efficiency of Apriori, FPGrowth
07
6
Clustering
Cluster Analysis, Types of Data, Categorization of Major Clustering
Methods, K- means, Partitioning Methods, Hierarchical Methods,
Density-Based Methods, Grid Based Methods, Outlier Analysis
07
7
Data Mining Applications
Financial Data Analysis & Marketing Industry, The Retail Industry, The
Telecommunication Industry
03
8
Case Study: Implementation of Data Mining Techniques with WEKA
02
Instructional Method and Pedagogy:


Lectures will be conducted on the basis of Classroom Response Systems with the
use of multimedia projector and black board.
Assignments based on course contents will be given at the end of each unit/topic
and will be evaluated at regular interval.
BCA, School of Computer Science, RK University
Students Learning Outcomes:
On the completion of the course, students will be able to:
 Differentiate between Data Warehouse and Database
 Use OLAP and OLTP systems for different applications
 Understand data analysis and data mining algorithms
 Understand and differentiate different data mining
transactional dataset
algorithms
on
the
Text books:

Title: Data Mining: Concepts & Techniques”, Morgan Kaufmann Publishers (2002)
Authors : " Jiawei Han & Micheline Kamber, “
Reference Books:

Title: " Building the Data Warehouse ", Wiley Dreamtech India Pvt. Ltd.,
Authors : W. H. Inmon,
 Title: "Design and Analysis of Algorithms”, 2nd Edition, Pearson Education
Authors: Parag Dave & Himanshu Dave (Publication Date: 2008)
 Title : “Introduction to Data Mining with Case Studies”, EEE, PHI (2006)
Authors : G. K. Gupta
Additional Resources


http://www.data-mining-guide.net/Data-Mining-Resources.html
http://en.wikipedia.org/wiki/Web_mining
BCA, School of Computer Science, RK University