DWM Anna University Notes

I want the notes of Data Warehousing and Data Mining. I am the student of Computer Science & Engineering 7th Semester of Anna University and I lost my notes of Data Warehousing and Data Mining so will you please provide me, it is very urgent?

Hello sir, Im student of Anna University looking for notes. Is there any one can provide me Data Ware Housing and Data Mining Anna University notes?

[pooja]

The Ana University has Data Ware Housing and Data Mining subject in Computer science engineering.

The Anna University is provides notes for Data Ware Housing and Data Mining.

A data warehouse is a subject oriented, integrated, time-variant, and non-volatile collection of data. This data helps analysts to take informed decisions in an organization.

The data ware house is the modern concept of database management system. The term data warehouse is given by W.H. Inmon.


Data Ware Housing and Data Mining Anna University notes
mediafire.com/file/pwsclpgfm6osouo/IT6702_notes_rejinpaul.pdf

Data warehouses are widely used in the following fields

Financial services
Banking services
Consumer goods
Retail sectors
Controlled manufacturing

IT6702 data ware housing and data mining theory syllabus regulation:

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 Back propagation Support Vector Machines Associative Classification Lazy Learners Other Classification Methods Prediction.

UNIT V CLUSTERING AND TRENDS IN DATA MINING
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