PredictiveApriori
Package
weka.associations
Synopsis
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is among the 'n' best and it is not subsumed by a rule with at least the same expected predictive accuracy (out of an unpublished manuscript from T. Scheffer).
Options
The table below describes the options available for PredictiveApriori.
Option |
Description |
|---|---|
car |
If enabled class association rules are mined instead of (general) association rules. |
classIndex |
Index of the class attribute. |
numRules |
Number of rules to find. |
Capabilities
The table below describes the capabilites of PredictiveApriori.
Capability |
Supported |
|---|---|
Class |
Missing class values, Nominal class, Binary class |
Attributes |
Missing values, Binary attributes, Empty nominal attributes, Unary attributes, Nominal attributes |
Min # of instances |
1 |