Download DMDW Course structure

Course Code and Title
CSL407 : Data Mining and Warehousing (DMW)
1. Course Description
Concept of Data mining and warehousing, applications to real life examples.
The study of data warehousing and various data mining tools.
3 lectures per week. Credit scheme - (L-T-P-C: 3-0-0-6)
2. Required Background or Pre-requisite: CSL403 : Database Management
Systems
3. Detailed Description of the Course
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Introduction to Data Mining and warehousing, real time applications, scope of
mining and warehousing for various applications.
(1 week)
Data warehousing- Various schema, three-tier architecture, design issues,
multidimensional model.
(2 week)
Datawarehouse development life cycle, Data warehouse analysis, CUBE,
ROLL UP and STAR queries.
(2 week)
Data Warehouse Design - Massive denormalisation, STAR schema design ,
Data ware house Architecture, OLAP, ROLAP and MOLAP , concepts of Fact
and dimension table
(2 week)
Space Management in Data warehouse - Schemas for storing data in
warehouse using different storage structures, B-tree index, hash index,
clusters, Bitmap index functional index, domain index, Data partitions.
(3 week)
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Performance and Tuning - Query optimization, memory management, process
management. I/o management for Data warehouse.
(2 week)
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Data Mining Tools - Association Rules , A priori Algorithms, Fp-trees
Algorithms,Constraints and solution.
(1 week)
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Cluster Analysis – Paradigms , DBSCAN , Cluster algorithms.
(1 week)
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Mining Tools - Decision Trees and applications.
(1 week)
4. Text books and/or other required material
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Data mining - Concepts & Techniques, Jiawei Han, Micheline Kamber,
Morgan Kaufmann ,2nd Ed.2006.
Oracle 8i Data Warehousing, Michale Corey, Michale Abbey, Tata McGraw
Hill
Fundamentals of Database Systems, Navathe and Elmasry, Addison Wesley,
2000
Data Mining, Arun Pujari Orient Longman, 2003
5. Course Objectives
1) Identify the scope and necessity of Data Mining & Warehousing for the
society.
2) Describe the designing of Data Warehousing so that it can be able to
solve the root problems.
3) To understand various tools of Data Mining and their techniques to
solve the real time problems.
4) To develop ability to design various algorithms based on data mining
tools.
5) To develop further interest in research and design of new Data Mining
techniques.
6. Class Schedule
Lectures : 3 1-hr lectures per week
7. Contribution of Course to Professional Component
Lecture: Students should know about design issues of data warehousing, learn
various mining tools, able to identify the real time problems and able to design
solution using various mining tools, further take the R&D interest and try to
contribute some new methods to the area.
8. Evaluation of Students
The instructor uses the following methods: 2 sessional exams, end-semester
exam, class test, some real time problems as programming assignments.
9. Relationship of Course Objectives to Program outcomes
The coorelation of the COs of the course Artificial Intelligence and the
POs are shown in the following table. A ‘H’, ‘M’, or ‘L’ mark on a cell
indicates whether the COs have a ‘high’, ‘medium’, or ‘low’ correlation
with the corresponding PO on the coloumn. A blank cell inidicates that
there is no correlation between the COs to a particular PO.
Correlation of COs of Data mining & Warehousing
PO 1 PO 2 PO 3 PO 4
H
H
L
PO 5
H
PO 6
H
PO 7
H
PO 8
M
PO 9
H
PO 10
L
PO 11
L
PO 12
H