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is among the 'n' best and it is not subsumed by a rule with at least the same expected predictive accuracy (out of an unpublished manuscript from T. Scheffer).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. If set to -1, the last attribute will be taken as 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

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