New components: k-Means (distance) and Same-size k-Means

Dear all,

I just wanted to share a couple of new k-Means components I have just added to the Hub.

The first one is k-Means (distance): it’s an extension of the native k-Means node, which outputs the euclidean distance between every node and its centroid, or the centroid of each cluster.

The second one is Same-size k-Means: it uses distances to force all clusters to have an equal number of points. Of course, by adding the “same-size” constraint, the clustering quality might degrade and be particularly prone to outliers. Still, in some circumstances, the component might be handy.

I’ve noticed that some older posts in the forum required these features, although they are closed now, so I can’t respond to them:

Any inputs and feedback are welcome!


Hi @AdM -

Thanks for posting these components - I’m sure they’re going to meet a need, given how many forum questions there have been about clustering. I went ahead and responded to those older closed forum posts with a link to this one.



Thank you @ScottF !



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