Uses functionality provided in a Jupyter notebook to embed documents from a topic-space representation into 2D Euclidean space. The embedding is done using scikit-learn's implementation of the t-SNE algorithm.
This is a companion discussion topic for the original entry at
The Jupyter Notebook seemingly did not make it into the final workflow, but it should be this one:
This file has been truncated.
"# Working with t-SNE from scikit-learn\n",
"I wanted to do an experiment and try embedding documents in 2D space so that the proximity can be used to identify clusters of related documents. I have a set of documents extracted from pubmed based on queries for disease names - Jeany described the construction of the dataset in her [Fun with Tags](https://www.knime.com/blog/fun-with-tags) blog post - and I've used those to build a topic model. I want to try the embedding using the projection of the documents into the topic space.\n",
And also if one would put it into a subfolder in the workflow the relative KNIME path should look something like this:
Thank you for this interesting workflow
mlauber71, we will add the missing file asap.