In a broad sense, we can evaluate the association rules using the same concept as for classification. We use a test set of data that was not used for training, and evaluate our discovered rules based on their performance in this test set.
To do this, we will compute the test set confidence, that is, the confidence of each rule on the testing set. We won't apply a formal evaluation metric in this case; we simply examine the rules and look for good examples.
Formal evaluation could include a classification accuracy by determining the accuracy of predicting whether a user rates a given movie as favorable. In this case, as described below, we will informally look at the rules to find those that are more reliable: ...
