12 when to use data mining

8/7/2019 12 When to Use Data Mining

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When to use Data Mining



Introduction

� An important question that should be answered before youcommence any data mining project is whether data miningtechniques are, in fact necessary.

� In determining this it is important to understand what levelof sophistication of data mining is required. For instance,do you just need a few standardized printed reports or doyou need interactive ROI analysis or OLAP analysis to seewhat your data looks like?

� Do you need or true data mining techniques that buildpredictive models to search through your database for useful patterns?



The Data Mining Process



What all Data Mining techniques

have in common� Each Data Mining algorithm has the following in

common:

± Model Structure. The structure that defines the model(Is it a tree, a neural network, or a neighbor?)

± Search. How does the algorithm amend and modify the

model over time as more data is made available

± Validation. When does the algorithm terminate becauseit has created a valid model?



What all Data Mining techniqueshave in common (cont¶d)



Data Mining in the Business Process� When Data Mining is used for non-exploratory

reasons or whenever supervised learning

techniques are used, this customer reactionprovide a fairly well-defined target column withinthe database, which relates to the business process.The target must have the following attributes inorder to be successful with data mining:

± The target has value

± The target is actionable

± The effect of action can be captured



Data Mining in the Business Process (cont¶d)



Avoiding some big mistakes in Data Mining

� The technology-centered view of the data

mining process emphasizes getting the

model right, with the assumption that the

predictive product has been well-defined

and that the data that has been captured to

date is well understood.� This is not always the case.



T

hree measures for Data MiningT

ools

� Accuracy. The data mining tool must produce a model thatis as accurate as possible.

� Explanation. The data mining tool needs to be able toµexplain¶ how the model works to the end user in a clear way

� Integration. The data mining tool must integrate with thecurrent business process, and data and information flow in

the company.� When these three requirements are well met, the datamining tools will produce highly profitable models that arelikely to remain stable over long periods of time.



Embedded Data Mining for business



How to measure Accuracy,

Explanation, and Integration� Measuring Accuracy:

± Accuracy

± Error rate

± Error rate at rejection

± Mean squared error

± Lift

± Profit/ROI



How to measure Accuracy,

Explanation, and Integration� Measuring Explanation:

± Automated rule generation

± OLAP integration

± Model validation

� Measuring Integrity

± Proprietary data extracts

± Metadata

± Predictor preprocessing

± Predictor/prediction types

± Dirty data

± Missing values

± Scalability



What the Future holds for

Embedded Data Mining� Once the data mining process becomes easy enough to use

and is seamlessly integrated into business process and the

general data and information flow around the enterprise,

there will be new applications and synergies that will make

data mining an even more critical requirement for any fully

functioning data warehouse

± Use data mining to improve the multidimensional database

± Use data mining to improve the data warehouse structure± Multidimensional databases and summary data will enhance data

mining performance. The more data, the better any data mining

technique is

12 when to use data mining

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