CS2032 DATA WAREHOUSING AND DATA MINING Lecture Notes for CSE - Seventh (7th) Semester -by R.Anirudhan

CS2032 DATA WAREHOUSING AND DATA MINING Lecture Notes for CSE - Seventh (7th) Semester


CS2032 Lecture Notes 

Syllabus :


UNIT I DATA WAREHOUSING
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
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
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
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
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.



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