RM World 2014: Design and implementation of data mining case studies

transcript

Dr. Matthew North

Professor of Business & Information Systems

The College of Idaho

RapidMinerWorld 2014

Boston, USA

Design and Implementation of Data

Mining Case Studies in RapidMiner

W&J/College of Idaho

A Focus on Teaching & Learning

Lots of folks do data mining

Lots of those use RapidMiner

Data mining education

Younger than the discipline

Strange collection of options

Science? Business? Math?

2005 – Present:

Books!

Tools!

Weka, Alphaminer, Clementine, more…

Education

Master’s, Certificates, Boot Camps

Data Mining for the Masses (2012)

Data Mining Cases in RapidMiner (2013)

The Case Method

Cases give context

My first Clementine class

Cases build on prior knowledge

Central Tendency > k-Means Clustering

Cases use Learning Theory

Concept Attainment

The Anatomy of a Data Mining Case

ActivationStimulate prior knowledge/learningRelevant to the data mining task

AdditionIntroduce the new conceptK-Means Clustering

ComparisonGood/poor examples

Conclusions

RapidMiner World/Boston Example

Activation: Welcome to Boston!

There’s a lot to do here

Lots of cool/smart people

After hours connections can be valuable

Can data mining help make an effective

fun/work connection?

Maybe so, if we rate options and then

build option clusters

Addition: Options + Data = Choice

List our options, then rate from 0-3

across various types of fun

Addition: Modeling the data

Comparison: What do you see?

Conclusions: So what?

Does this help you make a decision?

How can you fine tune your model?

To what other problems/datasets could

you apply what you’ve learned?

Response to Reviewers

Use of a toy example

Transfer of knowledge to other

scenarios is ideal

Sometimes a little help is good…

Loan Analyst Example

Activation:

You review loans looking for red flags

You know how to spot anomalies

Your work is time-consuming

Addition:

Problem loans don’t look like average ones

K-Means Clustering uses averages

Averages help create different groups

Loan Analyst Example

Comparison:Build a k-Means model with your loan data

You’re the expert, what do you see?Compare your standard method results to the

data mining results

Conclusions:Is the model useful?

Can it speed up your identification of problem loans?

Conclusions

Cases are fun/interesting

Cases are accessible to area experts

Learning data mining is often the hurdle

RapidMiner makes data mining

accessible to non-experts

Now…..

Who’s Ready to Hit the Town?!?

RM World 2014: Design and implementation of data mining case studies

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