CS2032 DATA WAREHOUSING AND DATA MINING Syllabus - Anna University


CS2032                      DATA WAREHOUSING AND DATA MINING                    L T P C
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UNIT I         DATA WAREHOUSING                                                                              10
Data warehousing Components Building a Data warehouse - Mapping the Data Warehouse to a Multiprocessor Architecture DBMS Schemas for Decision Support Data Extraction, Cleanup, and Transformation Tools Metadata.

UNIT II        BUSINESS ANALYSIS                                                                               8
Reporting and Query tools and Applications Tool Categories The Need for Applications Cognos Impromptu Online Analytical Processing (OLAP) Need – Multidimensional Data Model OLAP Guidelines Multidimensional versus Multirelational OLAP Categories of Tools OLAP Tools and the Internet.

UNIT III        DATA MINING                                                                                            8
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.

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UNIT IV       ASSOCIATION RULE MINING AND CLASSIFICATION                           11
Mining Frequent Patterns, Associations and Correlations Mining Methods – Mining Various  Kinds  of  Association  Rules   Correlation  Analysis   Constraint  Based Association Mining Classification and Prediction - Basic Concepts - Decision Tree Inductio - Bayesian Classification Rule Based Classification Classification by Backpropagation   Support  Vector  Machines   Associative  Classification   Lazy Learners Other Classification Methods - Prediction

UNIT V     CLUSTERING AND APPLICATIONS AND TRENDS IN DATA MINING        8
Cluster Analysis - Types of Data Categorization of Major Clustering Methods - K- means 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.

TOTAL: 45 PERIODS
TEXT BOOKS:

1.  Alex Berson and Stephen J. Smith, Data Warehousing, Data Mining & OLAP, Tata
McGraw Hill Edition, Tenth Reprint 2007.
2.  Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques, Second
Edition, Elsevier, 2007.


REFERENCES:

1.  Pang-Ning Tan, Michael Steinbach and Vipin Kumar, Introduction To Data Mining, Person Education, 2007.
2.  K.P. Soman, Shyam Diwakar and V. Ajay , Insight into Data mining Theory and
Practice, Easter Economy Edition, Prentice Hall of India, 2006.
3.  G. K. Gupta, Introduction to Data Mining with Case Studies, Easter Economy
Edition, Prentice Hall of India, 2006.
4.   Daniel T.Larose, “Data Mining Methods and Models, Wile-Interscience, 2006.




By Vinoth
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