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@sethah In general, MiniBatchKMeans generate a worse model, but in many
case the difference is small, while the speedup can be significant.
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@sethah Agree that if I/O is the bottleneck, the speedup should be small.
The cost in results doc is computed on the whole dataset, no the sampled
ones. Since I think comparing cost on
Github user sethah commented on the issue:
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Mini-batching in Spark generally isn't that efficient, since to extract a
mini-batch you still need to iterate over the entire dataset - and that means
reading it from disk if it doesn't fit into
Github user MLnick commented on the issue:
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cc @srowen @setha also
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@MLnick `@Since` updated. And the performance test result is attached in
the JIRA. Thanks for your reviewing!
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Github user MLnick commented on the issue:
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I commented on the JIRA about posting performance test results.
This should also be updated for `@Since` tags to `2.3.0`.
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Jenkins, retest this please
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