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.
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.
A zipped version of the software site can be downloaded here.
Do you have feedback, questions, comments about NodePit, want to support this platform, or want your own nodes or workflows listed here as well? Do you think, the search results could be improved or something is missing? Then please get in touch! Alternatively, you can send us an email to email@example.com, follow @NodePit on Twitter, or chat on Gitter!
Please note that this is only about NodePit. We do not provide general support for KNIME — please use the KNIME forums instead.