Hi Chitturi,

Please checkout
https://spark.apache.org/docs/1.0.1/api/java/org/apache/spark/mllib/clustering/KMeans.html#setInitializationSteps(int
).

I think it is caused by the initialization step. the "kmeans||" method does
not initialize dataset in parallel. If your dataset is large, it takes a
long time to initialize. Just changed to "random".

Hope it helps.


On Sun, Mar 13, 2016 at 2:58 PM Chitturi Padma <learnings.chitt...@gmail.com>
wrote:

> Hi All,
>
>   I  am facing the same issue. taking k values from 60 to 120 incrementing
> by 10 each time i.e k takes value 60,70,80,...120 the algorithm takes
> around 2.5h on a 800 MB data set with 38 dimensions.
> On Sun, Mar 29, 2015 at 9:34 AM, davidshen84 [via Apache Spark User List]
> <[hidden email] <http:///user/SendEmail.jtp?type=node&node=26467&i=0>>
> wrote:
>
>> Hi Jao,
>>
>> Sorry to pop up this old thread. I am have the same problem like you did.
>> I want to know if you have figured out how to improve k-means on Spark.
>>
>> I am using Spark 1.2.0. My data set is about 270k vectors, each has about
>> 350 dimensions. If I set k=500, the job takes about 3hrs on my cluster. The
>> cluster has 7 executors, each has 8 cores...
>>
>> If I set k=5000 which is the required value for my task, the job goes on
>> forever...
>>
>>
>> Thanks,
>> David
>>
>>
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Regards,
David

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