SimpleKMeans (3.7)

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.

For more information see:

D.Arthur, S.

Vassilvitskii: k-means++: the advantages of carefull seeding.In: Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms, 1027-1035, 2007.

(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.


SimpleKMeans Options

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)

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, Missing values, No class] Dependencies: [] min # Instance: 1

Command line options

It shows the command line options according to the current classifier configuration and mainly serves to support the node's configuration via flow variables.

Input Ports

Training data

Output Ports

Trained model

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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.


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