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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