[scikit-learn] Can I evaluate clustering efficiency incrementally?
ugoren at gmail.com
Fri May 3 07:27:21 EDT 2019
I usually use clustering to save costs on labelling.
I like to apply hierarchical clustering, and then label a small sample and
fine-tune the clustering algorithm.
That way, you can evaluate the effectiveness in terms of cluster purity
(how many clusters contain mixed labels)
See example with sklearn here :
On Fri, May 3, 2019, 11:03 AM 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.
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> scikit-learn at python.org
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