IT6702-DatawareHousing-and-DataMining

IT6702 DATA WAREHOUSING AND DATA MINING            L T P C                 3 0 0 3
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UNIT I                                   DATA WAREHOUSING                                                           9
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                                                          9
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                                                             9
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.

UNIT IV                ASSOCIATION RULE MINING AND CLASSIFICATION                          9
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 Back                                                                                                                            propagation – Support Vector Machines – Associative Classification – Lazy Learners – Other Classification                                                                                                                                        Methods – Prediction.
                                                                                                                           UNIT V                         CLUSTERING AND TRENDS IN DATA MINING                              9
                                                                                                                           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 and OLAP”, Tata McGraw
– Hill Edition, Thirteenth Reprint 2008.
2. Jiawei Han and Micheline Kamber, “Data Mining Concepts and Techniques”, Third Edition,
Elsevier, 2012.

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Vivekananthamoorthy N,
Oct 1, 2016, 10:30 PM
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Vivekananthamoorthy N,
Oct 1, 2016, 10:30 PM
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Vivekananthamoorthy N,
Oct 1, 2016, 10:30 PM
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Vivekananthamoorthy N,
Oct 1, 2016, 10:31 PM
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Vivekananthamoorthy N,
Oct 1, 2016, 10:30 PM
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Vivekananthamoorthy N,
Oct 1, 2016, 10:51 PM
ĉ
Vivekananthamoorthy N,
Oct 1, 2016, 10:29 PM
ĉ
Vivekananthamoorthy N,
Oct 1, 2016, 10:29 PM
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