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HierarchicalClusterer (3.7)

KNIME WEKA nodes (3.7) version 4.3.1.v202101261634 by KNIME AG, Zurich, Switzerland

Hierarchical clustering class. Implements a number of classic agglomorative (i.e

bottom up) hierarchical clustering methodsbased on .

(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.

Options

HierarchicalClusterer Options

D: If set, classifier is run in debug mode and may output additional info to the console

B: If set, distance is interpreted as branch length otherwise it is node height.

N: number of clusters

P: Flag to indicate the cluster should be printed in Newick format.

L: Link type (Single, Complete, Average, Mean, Centroid, Ward, Adjusted complete, Neighbor joining)

A: Distance function to use. (default: weka.core.EuclideanDistance)

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, Date attributes, String attributes, Missing values, No class] Dependencies: [] min # Instance: 0

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

Icon
Training data

Output Ports

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Trained model

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.

Installation

To use this node in KNIME, install KNIME Weka Data Mining Integration (3.7) from the following update site:

KNIME 4.3

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

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Developers

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