Keyword clustering - output in Jupyter Notebook

I need a solution for keyword clustering. The task must be done with Python/Jupyter Notebook.

Here's the original dataframe (see the attached pdf):

-there's hundreds of keywords.

-each keyword has a list of 10 urls associated with them

The ultimate goal is to aggregate keywords by similarity.

The following tasks need to be done:

1. Compare every list to all the other lists (example: Keyword#1 has 4 urls in common with Keyword#57)

2. Create a table for each keyword containing the all the other keywords and their level of similarity

3. Find a way to regroup keywords (using the method/python library of your choice), avoiding too much overlapping between groups (since keywords can belong to several clusters). Based on the similarity of the lists of urls (not lemmas)

4. Create a table for each cluster containing all the related keywords

The final result should be available through a Jupyter Notebook.

Each step should be documented.

Evner: Datasøgning, Maskinoplæring, Python

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Projekt ID: #18937310

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