Extracting hidden pattern Transformation Application No uncovery of data present

1. Data mining
- By Syed Jauwad

2. Contents
 Introduction
 Background
 Pre Processing
 Data mining
 Interpreting Results
 Applications

3. Introduction
 Extracting hidden pattern
 Transformation
 Application
 No uncovery of data present

4. Background
 Data converted to information
 Identifies trends within data
 New term , not technology
 Two separate processes
 Standards for data mining
 Softwares – Rapid Miner & Weka
 Knowledge discovery & data mining
 Knowledge discovery in database

5. Pre-processing
 Target must be large enough
 Target set is firstly clean
 Cleaned data is reduced to Feature vector

6. Data Mining
 Classification
 Clustering
 Regression
 Association Rule Learning

7. Interpreting results
 Final step is evalueation of the pattern
 All pattern not necessary valid
 If desired standard not meet
revalueation and mining
 If desired standard meet interprete
learn pattern turn them into knowledge

8. Application
 Combating terrorism
 Pattern mining
 Subject based data mining
 Games
 Science engineering
 Privacy concern and ethics