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Monday 20 May 2013

Dissertation Series - Data Mining Modelling Techniques


Within data mining there are various modelling techniques that can be undertaken, the type used will depend largely on the situation, data available and type of problem the organisation is trying to be solved/addressed.
Description
Description is the identification of trends/patterns within data, an example of such a trend would be that learners with poor attendance don’t achieve as highly as learners that have good attendance (Daniel, 2004 p.11).
Estimation
Estimation uses complete records to estimate an outcome.  An example of this would be to estimate this years income based on previous years income (Daniel, 2004 p.11).
Prediction
Prediction is similar to estimation although uses incomplete records to predict an outcome (Veerman et al, 2009 p.13).  An example of this would be the prediction of fourth quarter sales, based on the sales in the previous three quarters of the year (Daniel, 2004 p.11)
Classification
A classification modelling technique forecasts and groups outcome into a category/classification, for example weather being sunny or a learner going on to study at university level (Daniel, 2004 p.11).
Clustering
The technique of clustering involves grouping together those with similar characteristics; an example of clustering would be the grouping together of customers who go on to buy a certain type of product.  New customers that also appear within that cluster could be targeted for that certain type of product that other customers in that cluster purchased (Daniel, 2004 p.11).
Association
The technique of association is sometimes referred to as basket analysis and is the method used to forecast additional items that a customer may wish to buy based on items they purchased previously (Aberer, 2007)

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