Data mining- Introduction

Data Mining

Introduction to Data Analysis

Assessment is coursework= 50%

One on data mining

Survey on emerging topic within this area

See book: H. Du Data Mining Techniques and applications

Software used: WEKA

Data- isolated factual recording of separate object and events

Information- meaningful

Knowledge- using information for business

Data- supermarket sales

Information-coke often purchased along with crisps

Knowledge- put near each other

What is it

Useful info

Non-trivial implicit info- not raw nor result of simple data summary

Useful

Bank seeing credit card information’ salary or house

Would want house so if they cant pay you can take it

Non-trivial information

Online analytic processing- interactive reporting

Datamining- discovery of hidden embedded patterns

Real life databases

May be v big

Lots of different data types

Quality can be poor

Available on second storage media

Efficient algorithms

Use little memory and have  a quick execution time

May not be 100%

Objectives-

Classification

Estimation

Prediction

Description

WEKA

Investigative interactive data mining

Used on small data sets

WEKA explorer- main one we will use

WEKA knowledge flow- not covered

WEKA is available online (Free)