CS2032-Datawarehousing-and -DataMining

CS2032                 DATA WAREHOUSING AND DATA MINING L T P C              3 0 0 3 

 

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 Induction - 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 - 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.

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.

 

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Vivekananthamoorthy N,
May 16, 2015, 10:42 PM
ĉ
Vivekananthamoorthy N,
May 16, 2015, 10:42 PM
Ċ
Vivekananthamoorthy N,
May 16, 2015, 10:42 PM
Ċ
Vivekananthamoorthy N,
May 16, 2015, 10:43 PM
Ċ
Vivekananthamoorthy N,
May 16, 2015, 10:43 PM
ĉ
Vivekananthamoorthy N,
May 16, 2015, 10:43 PM
ĉ
Vivekananthamoorthy N,
May 16, 2015, 10:43 PM
Ċ
Vivekananthamoorthy N,
May 16, 2015, 10:42 PM
Ċ
Vivekananthamoorthy N,
May 16, 2015, 10:43 PM
ĉ
Vivekananthamoorthy N,
May 16, 2015, 10:43 PM
ĉ
Vivekananthamoorthy N,
May 16, 2015, 10:44 PM
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