Data Mining Business Hunching, not Data Crunching

11/24/98


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Table of Contents

Data Mining Business Hunching, not Data Crunching

The story board

The context of data mining

What is Data Mining?

Data holds Knowledge

What can we learn from data?

What is data mining?

An example Winterthur Insurance, Spain

Winterthur Insurance Application

Winterthur Approach

Winterthur Application

Winterthur Application

Winterthur “gains” curve

Data mining users - Finance

Manufacturing Utilities, Defence

Telecommunications

Pharma, Chemical, Retail

Government, Media, Consulting, IT

Halfords - Predicting Sales

Reuters Forex Data

Typical Applications

Typical Applications

Typical Applications

Typical Applications

The chasm of representation

The data is not the business

Fundamental DM Assumptions - 1

Many fuzzy things: hard to model

Hunching with the data

Fundamental DM Assumptions - 2

Business knowledge is crucial

Data Mining is easy

But data mining is complex

Many suppliers, hype, confusion

Data Mining is

Hydraulic data mining

Bikers and seafood?

Hydraulic data mining

Good Data Mining is:

Understand the Business Problem First

DM Process in a multi-level IS

Decision models

Models deployed in an IS Decision models (“agents”) in action

PPT Slide

Mysterious disappearing terabytes

PPT Slide

Data Mining Technology

Terminology

Data Exploration

Visualisation

Machine Learning

The Technology

Machine Learning

Neural Networks

Neural Networks

Problems

Rule Induction

Understanding Rule Trees

Rules & Nets

Clustering - Kohonen Networks

Some techniques are “automatic”

Some build executable models

Technology limitations

BBC TV Audience Prediction

Results Assessment

Danger of believing “explanations”

Rationale for ClementineTM

Bridging the chasm of action

Clementine: beware of imitations!

Now the real Clementine

“Hunching, not crunching”

Essay Question

The End

Author: Alan Montgomery