Icon

04_​Using_​Jupyter_​from_​KNIME_​to_​embed_​documents

This workflow uses functionality provided in a Jupyter notebook to embed documents from a topic-space representation into2D Euclidean space. The embedding is done using scikit-learn's implementation of the t-SNE algorithm.KNIME Extensions requirements: - Textprocessing - KNIME Python ScriptingThe Python Scripting integration needs to be configured to use a Python environment with scikit-learn and jupyter installed. Load data and build topic model Visualize results t-SNE embedding Embed using tSNERead documents Python Script (1⇒1) Topic Extractor(Parallel LDA) Create CollectionColumn Color Manager ReferenceRow Filter Joiner Interactivevisualization Table Reader Pick 10 randomqueries Preprocessdocuments This workflow uses functionality provided in a Jupyter notebook to embed documents from a topic-space representation into2D Euclidean space. The embedding is done using scikit-learn's implementation of the t-SNE algorithm.KNIME Extensions requirements: - Textprocessing - KNIME Python ScriptingThe Python Scripting integration needs to be configured to use a Python environment with scikit-learn and jupyter installed. Load data and build topic model Visualize results t-SNE embedding Embed using tSNERead documentsPython Script (1⇒1) Topic Extractor(Parallel LDA) Create CollectionColumn Color Manager ReferenceRow Filter Joiner Interactivevisualization Table Reader Pick 10 randomqueries Preprocessdocuments

Nodes

Extensions

Links

Download

To use this workflow in KNIME, download it from the below URL and open it in KNIME:

Download Workflow
Created by: jeany
Created at: 2018-06-13
On NodePit since: 2019-07-12
Last update: 2024-04-18
Created with KNIME version: v4.1.0

Deploy, schedule, execute, and monitor your KNIME workflows locally, in the cloud or on-premises – with our brand new NodePit Runner.

Try NodePit Runner!