Four methods are developed for data mining discrete multi-objective optimization datasets.

Two of the methods are unsupervised, one is supervised and the other is hybrid.

Knowledge is represented as patterns in one method, and as rules in other methods.

Methods are applied to three real-world production system optimization problems.

Extracted knowledge is compared across methods and provides new insights.