This document discusses data mining and provides details on key concepts like the data, information, knowledge hierarchy; business intelligence; the definition of data mining; the steps in the KDD (knowledge discovery in databases) process; techniques like classification, clustering, and association analysis; areas of application including finance, retail, telecommunications, and biology; and how data mining can be adapted for uses like market research, sales predictions, and inventory reports.

1. DATAMINING
YAMAN ÇAKMAKLAR

2. Content
Data, Information, Knowledge
Business Intelligence
Definition
Decisions
Steps
Area of Usage
KDD Process
Techniques
Data Warehouse
Adaptation

3. Data, Information, Knowledge

4. Business Intelligence
Operation Optimization
Analytics
Reporting
Collaboration
Knowledge Measurement

5. Definition
Deploying the Data Mining Model
Evaluation Phase
Building Data Mining Model
Preperation for Creating a Data Mining Model
Exploring Database
Creating Database
Problem Definition

6. Decisions
Databases to be mined
Knowledge to be mined
Techniques utilized
Applications adapted

7. Steps
Data integration
Data selection
Data cleaning
Data transformation
Data mining
Pattern evaluation
Knowledge presentation

8. KDD Process

9. Techniques
Classification and Prediction
Cluster Analysis
Outlier Analysis
Association Analysis
Evaluation Analysis

10. Data Warehouse

11. Area of Usage
Financial Data Analysis
Retail Industry
Telecommunication Industry
Biological Data Analysis
Other Scientific Applications
Intrusion Detection

12. Adaptation
Market Research
Product Research
Rival Company Research
Presales Predictions
Sales Objectives
Cross-sell Opportunties
Inventory Reports

13. Thank you for listening
YAMAN ÇAKMAKLAR