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SOTA Learner

KNIME Base Nodes version 4.3.2.v202103021015 by KNIME AG, Zurich, Switzerland

Clusters numerical and fuzzy data hierarchically with the self organizing tree algorithm and visualizes the cluster tree similarly like a dendrogram. Additionally it builds a model for class prediction of new data. The model can be load into the SOTA Predictor node.

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 dendrogram. The data is also displayed and can be hilit, as well as each cluster representative. Class information can be trained too by selecting the "Use class column" option for uses of class prediction by the SOTA Predictor node.

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

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 input table needs at least one integer column for this option being enabled. 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. Only integer columns can be chosen for the level.
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. The input table needs at least one string column for this option being enabled.
Class column
Specifies the column containing the class data. Only string columns can be chosen as class columns.

Input Ports

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Datatable with integer columns to do clustering on

Output Ports

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The model of the trained SOTA.

Views

SOTA Tree View
Displays the results of the SOTA clustering

Best Friends (Incoming)

Best Friends (Outgoing)

Installation

To use this node in KNIME, install KNIME Base nodes from the following update site:

KNIME 4.3

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

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