A Java implementation of the CBA algorithm
The classifier works with class association rules.That are association rules where exclusively one class attribute-value-pair is allowed in the consequence.
The algorithm works as a decision list classifier and has an obligatory and an optional pruning stepBoth steps can be disbaled.
If both are disbaled it works like a unpruned decision list und uses the first rule that covers a test instance for prediction.For more information see:
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.
Han, J.Pei: CMAR: Accurate and Efficient Classification Based on Multiple Class-Association Rules.In: ICDM'01, 369-376, 2001.
(based on WEKA 3.7)
For further options, click the 'More' - button in the dialog.
All weka dialogs have a panel where you can specify classifier-specific parameters.
A: Full class name of class association rule miner to include, followed by scheme options. Must produce class association rules. eg: "weka.associations.Apriori"
E: Enables CBA's optional pruning step (pessimistic-error-rate-based pruning) (default: no). Default confidence value is 0.25
V: If set class association rules are also part of the output(default no).
N: If set CBA's obligatory and optional pruning steps are both turned off (default: CBA's obligatory pruning step).
C: Sets the confidence value for pessimistic-error-rate-based pruning (default: 0.25).
The Preliminary Attribute Check tests the underlying classifier against the DataTable specification at the inport of the node. Columns that are compatible with the classifier are marked with a green 'ok'. Columns which are potentially not compatible are assigned a red error message.
Important: If a column is marked as 'incompatible', it does not necessarily mean that the classifier cannot be executed! Sometimes, the error message 'Cannot handle String class' simply means that no nominal values are available (yet). This may change during execution of the predecessor nodes.
Capabilities: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Missing values, Nominal class, Binary class, Missing class values] Dependencies: [Nominal attributes, Binary attributes, Unary attributes, Empty nominal attributes, Numeric attributes, Date attributes, String attributes, Relational attributes, Missing values, No class, Nominal class, Binary class, Unary class, Empty nominal class, Numeric class, Date class, String class, Relational class, Missing class values, Only multi-Instance data] min # Instance: 1
It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.
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To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.7) from the below update site following our NodePit Product and Node Installation Guide:
A zipped version of the software site can be downloaded here.
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