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FilteredAssociator (3.6)

KNIME WEKA nodes version 2.10.2.v201808081048 by KNIME AG, Zurich, Switzerland

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 3.6)

For further options, click the 'More' - button in the dialog.

All weka dialogs have a panel where you can specify associator-specific parameters.

Options

Class column
Choose the column that contains the target variable.
Preliminary Attribute Check

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

Associator Options

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)

c: The class index. (default = last)

Input Ports

Training data

Views

Weka Node View
Each Weka node provides a summary view that provides information about the classification. If the test data contains a class column, an evaluation is generated.

Update Site

To use this node in KNIME, install KNIME WEKA nodes from the following update site:

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