A simple meta-classifier that uses a clusterer for classification
For cluster algorithms that use a fixed number of clusterers, like SimpleKMeans, the user has to make sure that the number of clusters to generate are the same as the number of class labels in the dataset in order to obtain a useful model.
Note: at prediction time, a missing value is returned if no cluster is found for the instance.
The code is based on the 'clusters to classes' functionality of the weka.clusterers.ClusterEvaluation class by Mark Hall.
(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.
D: If set, classifier is run in debug mode and may output additional info to the console
W: Full name of 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, Missing values, Nominal class, Binary class] Dependencies:  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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