Lesson Plan F1.1-DMDW

SNSCT/IQAC/ F 1.1

SNS COLLEGE OF TECHNOLOGY COIMBATORE-35

DEPARTMENT OF INFORMATION TECHNOLOGYStaff InCharge : Dr.J.Shanthini Course : Data warehousing &Data Mining Semester : III Class : II Year M.Tech.ITAcademic Year : 2014-2015 - Odd Semester

LESSON PLANSl.No. Topic Method of

InstructionNo. of

PeriodsBook to be referred

UNIT I Data Warehousing and Business Analysis

1 Data warehousing Components –Building a Data warehouse Black board 1 R1

2 Mapping the Data Warehouse to a Multiprocessor Architecture

PPT , Black board 1 R1

3 DBMS Schemas for Decision Support Black board 1 R1

4 Data Extraction, Cleanup Black board 1 R1

5 Transformation Tools Black board 1 R1

6 Metadata, Query reporting tools and Applications Black board 1 R1

7 Online Analytical Processing (OLAP) PPT Black board 2 R1

8 OLAP and Multidimensional Data Analysis. Black board 1 R1

9 Revision & University QP Discussion - 1 -

Unit I 10

UNIT II Data Mining1 Data Mining Functionalities Black board 1 R2

2 Data Preprocessing – Data Cleaning Group Discussion 1 R2

3 Data Integration and Transformation Black board 1 R2

4 Data Reduction – Data Discretization and Concept Hierarchy Generation. Black board 1 R2

5 Association Rule Mining Black board 1 R2

6 Efficient and Scalable Frequent Item set Mining Methods PPT 1 R2

7 Mining Various Kinds of Association Rules Black board 1 R2

8 Association Mining to CorrelationAnalysis Black board 1 R2

9 Constraint-Based Association Mining. Black board 1 R2


Unit II 10

UNIT III Classification and Prediction

1 Issues Regarding Classification and Prediction Black board 1 R7

2 Classificationby Decision Tree Introduction Black board 1 R7

3 Bayesian Classification- Rule Based Classification OHP 1 R7

SNSCT/IQAC/ F 1.1

4 Classification by Back propagation – Support Vector Machines OHP 1 R7

5 Associative Classification – Lazy Learners PPT 2 R7

6 Other Classification Methods – Prediction- Accuracy and Error Measures PPT 1 R7

7 Evaluating the Accuracy of a Classifier or Predictor Black board 1 R7

8 Ensemble Methods – Model Section Black board 1 R7


Unit III 10

UNIT IV Cluster Analysis1 Types of Data in Cluster Analysis Black board 1 R6

2 A Categorization of Major ClusteringMethods Black board 1 R6

3 Partitioning Methods – Hierarchical methods PPT 1 R6

4 Density-Based Methods – Grid-BasedMethods OHP 1 R6

5 Model-Based Clustering Methods Black board 1 R6

6 Clustering High Dimensional Data Black board 1 R6

7 Clustering with constraints – PPT 1 R6

8 Outlier Analysis and detection methods. PPT 1 R6


Unit IV 10

UNIT V Mining Object, Spatial, Multimedia, Text and Web Data

1Multidimensional Analysis andDescriptive Mining of Complex Data Objects

Black board 1 R3

2 Spatial Data Mining Black board 1 R2

3 Multimedia Data Mining Black board 1 R2

4 Text Mining OHP 1 R2& R3

5 Applications and trends in data mining LCD 1 R2

6 Data Mining tools: WEKA Black board 1 R3

7 Data Mining tools: RapidMiner Black board 1 R3

8 Big Data. Black board 1 R4


Unit V 10 Total Hours : 45[Lecture]+5[Revision]=50

REFERENCES:R1 Alex Berson and Stephen J. Smith “Data Warehousing, Data Mining & OLAP”, Tata

McGraw – Hill Edition, Thirteenth Reprint 2008.R2 Jiawei Han and Micheline Kamber “Data Mining Concepts and Techniques” Elsevier,

Third Edition, print 2011R3 Ian H. Witten, Eibe Frank, Mark A. Hall “Data Mining: Practical Machine Learning

Tools and Techniques” Elsevier 2011.R4 Pete Warden, “Big Data Glossary”,O’Reilly , 2011R5 M.Golfarelli, S.Rizzi,” Data warehouse Design: Modern Principles and Methodologies”,

SNSCT/IQAC/ F 1.1

McGraw-Hill, 2009.R6 Margaret H.Dunham,”Data Mining: Introductotry and Advanced Topics”, Prentice Hall,

2003.R7 Pang-Ning Tna, Michael Stunbach and Vipin Kumar,” Introduction to Data mining”

Pearson Addison Wesley, 2005.R8 Viktor Mayer-Schonberger, Kenneth Cukier, “Big Data: A Revolution That Will

Transform How We Live, Work, and Think”, 2013.

Staff In Charge HOD Principal

Documents

Lesson Plan F1.1-DMDW