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What you are describing is close to a K-means.

But (classic) K-means is based on square of the distance and cluster size is not fixed. So you would need to adjust your criteria to comply with K-means or modify the algorithm.

If you don't want to take the distance squared the algorithm gets more complex.

What you are describing is close to a K-means.

But (classic) K-means is based on square of the distance and cluster size is not fixed. So you would need to adjust your criteria to comply with K-means or modify the algorithm.

What you are describing is close to a K-means.

But (classic) K-means is based on square of the distance and cluster size is not fixed. So you would need to adjust your criteria to comply with K-means or modify the algorithm.

If you don't want to take the distance squared the algorithm gets more complex.

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source | link

What you are describing is close to a K-means.

But (classic) K-means is based on square of the distance and cluster size is not fixed. So you would need to adjust your criteria to comply with K-means or modify the algorithm.