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is among the 'n' best and it is not subsumed by a rule with at least the same expected predictive accuracy (out of an unpublished manuscript from T. Scheffer).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.
The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.
Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.
Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Missing values, No class, Nominal class, Binary class, Missing class values] Dependencies:  min # Instance: 1
N: The required number of rules. (default = 100)
A: If set class association rules are mined. (default = no)
c: The class index. (default = last)
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To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.6) from the below update site following our NodePit Product and Node Installation Guide:
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