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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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