Github user yanboliang commented on the issue:
https://github.com/apache/spark/pull/14937
@sethah Yeah, I agree it's better to run more test against large-scale
data. If the number of feature or cluster is large, the center array slice cost
and some other place can be optimized which I did not pay more attention. And
we definitely should really understand the performance test result. So feel
free to share your result.
When I did this optimization, we found ```KMeans``` was usually used when
the number of feature is not too large. If users have a high-dimensional data,
they usually reduce feature dimension by ```PCA```, ```LDA``` or similar
algorithms and then feed them into ```KMeans``` for clustering. So the
optimization should be more focus on not very high dimensional data if we can
not guarantee better performance for any cases. However, it's well if we can
figure out one way to benefit both cases. Thanks.
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