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.
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