Class for running an arbitrary clusterer on data that has been passed through an arbitrary filter
Like the clusterer, 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.AllFilter)
W: Full name of base clusterer. (default: weka.clusterers.SimpleKMeans)
N: number of clusters. (default 2).
P: Initialize using the k-means++ method.
V: Display std. deviations for centroids.
M: Don't replace missing values with mean/mode.
A: Distance function to use. (default: weka.core.EuclideanDistance)
I: Maximum number of iterations.
O: Preserve order of instances.
fast: Enables faster distance calculations, using cut-off values. Disables the calculation/output of squared errors/distances.
num-slots: Number of execution slots. (default 1 - i.e. no parallelism)
S: Random number seed. (default 10)
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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