Package

weka.associations

Synopsis

Class implementing an Apriori-type algorithm. Iteratively reduces the minimum support until it finds the required number of rules with the given minimum confidence.
The algorithm has an option to mine class association rules. It is adapted as explained in the second reference.

For more information see:

R. Agrawal, R. Srikant: Fast Algorithms for Mining Association Rules in Large Databases. In: 20th International Conference on Very Large Data Bases, 478-499, 1994.

Bing Liu, Wynne Hsu, Yiming Ma: Integrating Classification and Association Rule Mining. In: Fourth International Conference on Knowledge Discovery and Data Mining, 80-86, 1998.

Options

The table below describes the options available for Apriori.

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 is taken as class attribute.
delta	Iteratively decrease support by this factor. Reduces support until min support is reached or required number of rules has been generated.
lowerBoundMinSupport	Lower bound for minimum support.
metricType	Set the type of metric by which to rank rules. Confidence is the proportion of the examples covered by the premise that are also covered by the consequence(Class association rules can only be mined using confidence). Lift is confidence divided by the proportion of all examples that are covered by the consequence. This is a measure of the importance of the association that is independent of support. Leverage is the proportion of additional examples covered by both the premise and consequence above those expected if the premise and consequence were independent of each other. The total number of examples that this represents is presented in brackets following the leverage. Conviction is another measure of departure from independence. Conviction is given by
minMetric	Minimum metric score. Consider only rules with scores higher than this value.
numRules	Number of rules to find.
outputItemSets	If enabled the itemsets are output as well.
removeAllMissingCols	Remove columns with all missing values.
significanceLevel	Significance level. Significance test (confidence metric only).
upperBoundMinSupport	Upper bound for minimum support. Start iteratively decreasing minimum support from this value.
verbose	If enabled the algorithm will be run in verbose mode.

Capabilities

The table below describes the capabilites of Apriori.

Capability	Supported
Class	Missing class values, Binary class, No class, Nominal class
Attributes	Empty nominal attributes, Unary attributes, Binary attributes, Missing values, Nominal attributes
Min # of instances	1

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