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WekaAPriori

Mines association rules using a priori algorithm: 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. Option for class association rules: 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.

Version 1.1
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Authors Sirkku Karinen (sirkku.karinen@helsinki.fi)
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Requires weka.jar (jar) ; csbl-javatools.jar (jar) ; installer (bash)
Source files component.xml
Usage Example with default values

Inputs

Name Type Mandatory Description
in CSV Mandatory Transaction data with categorical variables. Transactions are presented by rows, and data can not contain numeric variables.
selectedColumns CSV Optional List of selected columns used for association mining. Each row has comma-separated list and each row is processed in turn. Results are combined into single output.

Outputs

Name Type Description
out CSV Association rules

Parameters

Name Type Default Description
classCol string "" Class column in the data
classRules boolean false Flag for mining class association rules
maxRules int (no default) Maximum number of rules
maxSup float 1 Maximum support
minConfidence float (no default) Minimum confidence (i.e. conditional probabilities for rules)
minSup float (no default) Minimum support
removeCols string "" Columns to be removed from the data.

Test cases

Test case Parameters IN
in
IN
selectedColumns
OUT
out
case1 properties in (missing) out

minSup=0.1,
maxSup=1,
minConfidence=0.5,
maxRules=20,
removeCols=Id

case2_class properties in (missing) out

minSup=0.1,
maxSup=1,
minConfidence=0.5,
maxRules=20,
removeCols=Id,
classRules=true,
classCol=C3

case3_columns properties in selectedColumns out

minSup=0.1,
maxSup=1,
minConfidence=0.5,
maxRules=20,
removeCols=Id


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