SOTA Learner (deprecated)

This Node Is Deprecated — This node is kept for backwards-compatibility, but the usage in new workflows is no longer recommended. The documentation below might contain more information.

This node is obsolete! Please use the new SOTA Learner node located in the Mining -> Clustering category.

Clusters numerical and fuzzy data hierarchically with the self organizing tree algorithm and visualizes the cluster tree similarly like a dendogram.

The SOTA Learner node has a dialog, in which you can choose the winner, ancestor and sister learning rate, to adjust the cluster representants: with the minimal resource and variability value to stop the growing of the tree; the minimal error, to end a cycle and the distance metric (cosinus, euclidean). The node will cluster the given data hierarchically by use of the self organizing tree algorithm and will produce a cluster tree, which is visualized by the view afterwards, similar to a dendogram. The data is also displayed and can be hilit, as well as each cluster representative.

For more information about the SOTA clustering see: Herrero J., Valencia A., Dopazo J.: A hierarchical unsupervised growing neural network for clustering gene expression patterns.

Options

Column filter: Choose only these columns for the clustering process which are important and interesting. Columns which are not chosen will be ignored. Be aware that columns containing fuzzy cells and columns containing number cells can not be mixed.
Winner learningrate: Set the winners learning rate which is used to adjust the winning cluster representative to the given data.
Sister learningrate: Set the sisters learning rate which is used to adjust the sister-cluster representative of the winner to the given data.
Ancestor learningrate: Set the ancestors learning rate which is used to adjust the ancestor-cluster representative of the winner to the given data.
Minimal variability: Set the minimal variability value, which is the threshold for stopping the growing of the tree, if the "Use variability" checkbox is checked. If minimal variability is set to 0 the clustering will not stop before each datapoint is represented by a cluster representative.
Minimal resource: Set the minimal resource value, which is the threshold for stopping the growing of the tree, if the "Use variability" checkbox is not checked. The resource value is used as threshold to stop the growing by default. If minimal resource is set to a very small number i.e. 0.01 the clustering will not stop before each datapoint is represented by a cluster representant. The minimal resource value cannot be set to 0 but only to very small numbers.
Minimal error: Set the minimal error value to control the epochs per cycle. If the minimal error value is small, more epochs will be needed to end a cycle. This means, that all cluster representatives will be pulled closer to their data.
Use variability: Check this if the minimal variability value should be used to stop the growing of the tree, or leave it unchecked to use the minimal resource value. Be aware that the computation of the minimal variability value is very time consuming when clustering large amounts of data. The complexity is n*n.
Distance metric: Choose the euclidean or cosinus distance metric, to measure the distance between datapoints and cluster representatives.
Use hierarchical fuzzy data: Check to cluster hierarchical fuzzy rules hierarchically. The column with the fuzzy rule level has to be selected in the drop down menu on the right. The drop down menu will be enabled when the checkbox is checked.
Hierarchical level: If the checkbox on the left is checked, to cluster hierarchical fuzzy rules, the column with the fuzzy rule level must be chosen by selecting it in the drop down menu.
Use class column: Check to use the class data specified by the drop down box below. The class data will be trained too for the use of class prediction by the SOTA Predictor node.
Class column: Specifies the column containing the class data. Only string columns can be chosen as class columns.

Input Ports

: Datatable with integer columns to do clustering on

Output Ports

This node has no output ports

Popular Predecessors

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Popular Successors

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Views

SOTA Tree View: Displays the results of the SOTA clustering

Workflows

No workflows found

Developers

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Installation

To use this node in KNIME, install the extension KNIME Base nodes 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: 5.3.3.v202410291553

On NodePit since: 2024-07-09

Last update: 2024-11-21

Tags: Deprecated

KNIME versions: Since v4.2

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