[scikit-learn] Can I evaluate clustering efficiency incrementally?

[Guillaume Lemaître]

You can always predict incrementally by predicting on batches of samples.

On Fri, 3 May 2019 at 10:05, lampahome <pahome.chen at mirlab.org> wrote:

> I see some algo can cluster incrementally if dataset is too huge ex:
> minibatchkmeans and Birch.
>
> But is there any way to evaluate incrementally?
>
> I found silhouette-coefficient and Calinski-Harabaz index because I don't
> know the ground truth labels.
> But they can't evaluate incrementally.
> _______________________________________________
> scikit-learn mailing list
> scikit-learn at python.org
> https://mail.python.org/mailman/listinfo/scikit-learn
>


-- 
Guillaume Lemaitre
INRIA Saclay - Parietal team
Center for Data Science Paris-Saclay
https://glemaitre.github.io/
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mail.python.org/pipermail/scikit-learn/attachments/20190503/92f09ca2/attachment.html>