The TwoStep-AS node provides a form of cluster analysis, with distributed processing for handling very large amounts of data. It can be used to cluster the dataset into distinct groups when you don’t know what those groups are at the beginning.
- For more information about this node, see TwoStep-AS Overview.
- For more information about the visualizations for this node, see TwoStep-AS Visualizations.
As with Kohonen nodes and K-Means nodes, TwoStep-AS clustering models do not use a target field (and are thus forms of unsupervised learning). Instead of trying to predict an outcome, TwoStep-AS tries to uncover patterns in the set of input fields. Records are grouped so that records within a group or cluster tend to be similar to each other, but records in different groups are dissimilar.
Like your visualization? Why not deploy it? For more information, see Deploy a model.