Scikit-learn's HDBSCAN Implementation

[MaartenGr]

In a recent version of scikit-learn, I believe it was v1.3, HDBSCAN was implemented with base functionality. Considering scikit-learn is already a requirement of BERTopic it stands to reason to use that implementation instead of the original implementation since scikit-learn has more contributors. Moreover, common installation issues related to HDBSCAN might be alleviated with this.

There are a couple of issues worth mentioning:

• Calculation of probabilities is if I'm not mistaken, not implemented in scikit-learn's HDBSCAN
  • A solution would be to use the cosine similarities as the default method of calculating probabilities
• The feature set is smaller than the original implementation
• Speed needs to be tested to identify whether this is worth it
• Accuracy, whatever that means in this context, might also need some exploration

For those reading this, I'm interested to hear what you all think about this suggested change!

[joshhilton]

Anything that simplifies the dependency structure is progress. Would it affect the use of the cuML implementation? If not, you could leave that as the faster alternative.

[MaartenGr]

It doesn't affect the cuML implementation since it should be nothing more than a drop-in replacement of the original implementation. One major disadvantage is that currently soft-clustering is not implemented in the scikit-learn version which makes it difficult to return probabilities.