Class for running an arbitrary associator on data that has been passed through an arbitrary filter
Like the associator, the structure of the filter is based exclusively on the training data and test instances will be processed by the filter without changing their structure.
(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.
F: Full class name of filter to use, followed by filter options. eg: "weka.filters.unsupervised.attribute.Remove -V -R 1,2" (default: weka.filters.MultiFilter with weka.filters.unsupervised.attribute.ReplaceMissingValues)
c: The class index. (default: -1, i.e. unset)
W: Full name of base associator. (default: weka.associations.Apriori)
N: The required number of rules. (default = 10)
T: The metric type by which to rank rules. (default = confidence)
C: The minimum confidence of a rule. (default = 0.9)
D: The delta by which the minimum support is decreased in each iteration. (default = 0.05)
U: Upper bound for minimum support. (default = 1.0)
M: The lower bound for the minimum support. (default = 0.1)
S: If used, rules are tested for significance at the given level. Slower. (default = no significance testing)
I: If set the itemsets found are also output. (default = no)
R: Remove columns that contain all missing values (default = no)
V: Report progress iteratively. (default = no)
A: If set class association rules are mined. (default = no)
Z: Treat zero (i.e. first value of nominal attributes) as missing
B: If used, two characters to use as rule delimiters in the result of toString: the first to delimit fields, the second to delimit items within fields. (default = traditional toString result)
c: The class index. (default = last)
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, 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] 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: 0
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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