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Friday, October 12, 2012

M.E./ DATA WAREHOUSING AND MINING NOTES


Definition of Data warehouse:


Data Warehouse Components:


Building a Data warehouse:


Data Integration and transformation

Classical Encryption

Mapping the data warehouse to a multiprocessor architecture

Database schema for decision support system:

Data Extraction, Transformation, and Migration Tools:

metadata and reporting

Query tools and implementation:

OLAP and multidimensional data analysis

Data Mining Functionalities:

Data Preprocessing-Data Cleaning

Data Integration and transformation

 Data reduction:

Data Discretization and Concept hierarchy Generation

Association Rule Mining


Efficient and scalable methods for mining frequent patterns

  Mining Various Kinds of Association Rules

Constraint based Association mining

Support Vector Machines

Associative Classification

Lazy Learners (or Learning from Your Neighbors)

Accuracy and Error Measures:

Evaluating the Accuracy of a classifier

Ensemble Learning and Model Selection

Cluster Analysis:

-Type of data in cluster analysis:

clustering methods

Hierarchical and partition based clustering


Density based and Grid based clustering


Model based Clustering:

Clustering high dimensional data

Constraint based cluster analysis

Outlier Analysis:   

Constraint-Based Clustering:

Spatial Data mining:

Outlier Analysis:   

Object data mining

Multimedia Data mining:

Text Data Mining:

 Web Data Mining

Multidimensional Data Analysis:

Descriptive mining of complex data objects

Clustering tools

- Weka tool implementation

R-software introduction

R-software execution for Model based clustering

2 comments:

  1. Excellent article. Thanks for sharing.
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