Cluster data using the k means algorithm. Can use either the Euclidean distance (default) or the Manhattan distance. If the Manhattan distance is used, then centroids are computed as the component-wise median rather than mean.
(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
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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