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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