This document discusses data mining and is divided into several sections. It introduces data mining as extracting hidden patterns from data and transforming that data into useful information. It covers preprocessing data by cleaning and reducing it, common data mining techniques like classification, clustering and regression, and interpreting and evaluating the results. Finally, it discusses applications of data mining such as combating terrorism, pattern mining, and its use in science, engineering and games.

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