Class implementing the predictive apriori algorithm to mine association rules. It searches with an increasing support threshold for the best 'n' rules concerning a support-based corrected confidence value. For more information see: Tobias Scheffer: Finding Association Rules That Trade Support Optimally against Confidence. In: 5th European Conference on Principles of Data Mining and Knowledge Discovery, 424-435, 2001. The implementation follows the paper expect for adding a rule to the output of the 'n' best rules. A rule is added if: the expected predictive accuracy of this rule is among the 'n' best and it is not subsumed by a rule with at least the same expected predictive accuracy (out of an unpublished manuscript from T. Scheffer).

(based on WEKA 3.6)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify associator-specific parameters.