yuhao yang created SPARK-19957:
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Summary: Inconsist KMeans initialization mode behavior between ML
and MLlib
Key: SPARK-19957
URL: https://issues.apache.org/jira/browse/SPARK-19957
Project: Spark
Issue Type: Bug
Components: ML
Affects Versions: 2.1.0
Reporter: yuhao yang
Priority: Minor
when users set the initialization mode to "random", KMeans in ML and MLlib has
inconsistent behavior for multiple runs:
MLlib will basically use new Random for each run.
ML Kmeans however will use the default random seed, which is
{code}this.getClass.getName.hashCode.toLong{code}, and keep using the same
number among multiple fitting.
I would expect the "random" initialization mode to be literally random.
There're different solutions with different scope of impact. Adjusting the
hasSeed trait may have a broader impact. We can always just set random default
seed in KMeans.
Appreciate your feedback.
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