Class for wrapping a Clusterer to make it return a distribution and density. Fits normal distributions and discrete distributions within each cluster produced by the wrapped clusterer. Supports the NumberOfClustersRequestable interface only if the wrapped Clusterer does.
(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 clusterer-specific parameters.
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, No class] Dependencies:  min # Instance: 1
M: minimum allowable standard deviation for normal density computation (default 1e-6)
W: Clusterer to wrap. (default weka.clusterers.SimpleKMeans)
N: number of clusters. (default 2).
V: Display std. deviations for centroids.
M: 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.
S: Random number seed. (default 10)
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To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.6) 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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