Is there a really simple description of the practical differences between these two techniques?
Both seem to be used for supervised learning (though association rules can also handle unsupervised).
Both can be used for prediction
...detect relationships or associations between specific values of categorical variables in large data sets.
Whilst Decision Tree classifiers are described as being used to:
...predict membership of cases or objects in the classes of a categorical dependent variable from their measurements on one or more predictor variables.
However, over at R Data Mining, they give an example of Association Rules being used with a target field.
So both can be used to predict group membership, is the key difference that decision trees can handle non-categorical input data whilst association rules can't? Or is there something more fundamental? One site (sqlserverdatamining.com) says that the key difference is:
The decision trees rules are based on information gain while association rules are based on popularity and/or confidence.
So (possibly answering my own question) does that mean that association rules are evaluated purely on how often they appear in the dataset (and how often they are 'true') whilst decision trees are actually trying to minimise variance?
If anyone knows of a good description they'd be willing to point me towards then that would be great.