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PredictiveApriori (3.6) (legacy)

AnalyticsIntegrationsWekaWeka (3.6)Association Rules
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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is among the 'n' best and it is not subsumed by a rule with at least the same expected predictive accuracy (out of an unpublished manuscript from T. Scheffer).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.

Node details

Input ports
  1. Type: Table
    Training data
    Training data

Extension

The PredictiveApriori (3.6) (legacy) node is part of this extension:

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