This document compares data mining and data warehousing. It defines data mining as the process of analyzing trends and patterns in data through automated algorithms to explore and analyze data. Data warehousing is defined as aggregating and storing data in a common repository after cleaning errors. The document outlines key differences such as data mining being automated while data warehousing requires data cleaning, and provides examples of how businesses use each technique.

2. Data Mining VS Data Warehousing
• Data Mining and Data Warehousing both are used to holds business intelligence
and enable decision making.

3. What is Data Minning ?
Also known as
knowledge discovery in
data (KDD).
Analyze trends
and patterns in
data
Done
automatically
using algorithm
Data Exploration
and Data Analysis

4. Who uses Data Mining ?
For Profit businesses
• Increases Profits.
• Improves customer experiences and loyalty.
Non-Profit businesses and charities
• Target correct group of people.
Example of how data mining is used
• Retail Stores
• Performing basket analysis
• Sales Forecasting
• Database Marketing

5. Process of Data Mining ?
They are six main steps in data mining
• Anomaly detection - Identify data that is unusual or considered errors.
• Association Rule – Searching for relationships between different parts of
the data.
• Clustering- Finding structures and relationships between different parts
of the data.
• Classification- placing new data among similar groups.
• Regression – Finding a common formula to model the data.
• Summarization – Making visual or written description of the data.

6. What is Data Warehousing ?
• Process of aggregating data and storing in a common
repository
• Data must be cleaned to correct errors and eliminate
noise

7. Top down
Bottom
up
Hybrid
design
Three Design Methodology

8. Data Warehousing Cont.
Several methods of organizing data for access.
• Data Mart
• Online Analytical Processing
• Online Transaction Processing
• Predictive Analysis

9. Data Warehouse Features
Subject Oriented Integrated
Time Variant Non-Volatile

10. Why a separate Data Warehouse ?
High performance for both system.
• DBMS
• Warehouse
Different function and different data
• Missing data
• Data consolidation
• Data quality

11. Snow flake
schema
Star
Schema
Fact
Constellations
Conceptual Modelling of Data Warehouse

12. THANK YOU!