This workflow demonstrates the use of functionality defined in a Jupyter notebook from inside of KNIME. After building a topic model for a set of documents, we use a Python function from a Jupyter notebook to perform t-SNE embedding of the documents into a 2D space. As a final step we do an interactive visualization to allow exploring the results. The execution of this workflow requires the Textprocessing and the KNIME Python Scripting extensions. The executing Python environment must be provided with scikit-learn and jupyter installations.
Used extensions & nodes
Created with KNIME Analytics Platform version 4.1.0
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