SimpleKMeans (3.6) (legacy)

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.

Options

Class column
Choose the column that contains the target variable.
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

Clusterer Options

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)

Input Ports

Icon
Training data

Output Ports

Icon
Trained clusterer

Popular Predecessors

  • No recommendations found

Popular Successors

  • No recommendations found

Views

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.

Workflows

  • No workflows found

Links

Developers

You want to see the source code for this node? Click the following button and we’ll use our super-powers to find it for you.

Installation

To use this node in KNIME, install the extension KNIME Weka Data Mining Integration (3.6) (legacy) from the below update site following our NodePit Product and Node Installation Guide:

v5.3

A zipped version of the software site can be downloaded here.

Plugin provider: KNIME AG, Zurich, Switzerland
Plugin version: 2.10.2.v202312081535
On NodePit since: 2024-07-09
Last update: 2024-11-21
KNIME versions: Since v5.2

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!