Data mining is the process of discovering useful patterns from large amounts of data using statistical, mathematical, and artificial intelligence techniques. It involves applying these techniques to extract and identify useful information from large datasets. Data mining draws from multiple disciplines including statistics, pattern recognition, mathematical modeling, information systems, and machine learning. It has various applications in domains such as customer relationship management, banking, retailing, manufacturing, insurance, software, government, travel, and healthcare. The CRISP-DM process provides a standard methodology for data mining projects involving six steps: business understanding, data understanding, data preparation, modeling, evaluation, and deployment.

1. OMega TechEd
7

2. BUSINESS INTELLIGENCE
DATA MINING
Mrs. Megha Sharma
M.Sc. Computer Science. B.Ed.

3. DATA MINING
Data Mining is a discovering or “mining” knowledge
from large amount of data.
Data mining is a process that uses statistical ,
mathematical, and artificial intelligence techniques
to extract and identify useful information and
subsequent knowledge from large sets of data.

4. Data Mining as a Blend of Multiple Disciplines
Statistics
1
Pattern Recognition
2
Mathematical Modelling
3
Management Information System
4

5. How Data Mining Works.

6. Data Mining Applications.
 Customer relationship management.
Banking.
Retailing and Logistics.
Manufacturing and Production
Insurance.
Computer hardware and Software.
Government and Defence.
Travel Industry.
Health care and Medicine.

7. Six-step CRISP-DM. CRoss-Industry Standard Process for Data Mining
Business
Understanding
Data
Understanding
Data
Preparation
Model Building
Testing and
Evalution
Deployment
Data
Source

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