Hey I'd like to take up this @Radu1999

Hey I'd like to take up this @Radu1999

No, it's ok, I was planning to implement it once I confirm there is interest for it.

Fairs go for it then

@scikit-learn/core-devs any interest in this?

@Radu1999 to help evaluate this, could you provide some references and context that helps answer the questions from https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms

+1

@Radu1999 to help evaluate this, could you provide some references and context that helps answer the questions from https://scikit-learn.org/stable/faq.html#what-are-the-inclusion-criteria-for-new-algorithms

@betatim the paper has more than 200+ citations and it is published in 2012..
IMO it excels at clustering normalized, directional data (like text), where vector direction matters more than magnitude.

@betatim I'll like to take this!

I'd say with a small maintainable implementation, I'd be happy to have it.

I'd suggest adding configurable distance metric with 'euclidean' by default into existing Kmeans, rather than implementing a separate class. Just like here: https://spark.apache.org/docs/latest/api/python/_modules/pyspark/ml/clustering.html#KMeans