[ 
https://issues.apache.org/jira/browse/MAHOUT-121?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12720123#action_12720123
 ] 

Shashikant Kore commented on MAHOUT-121:
----------------------------------------

Apologies for posting incorrect results in previous comments.  I applied Sean's 
patch to the code which had optimization fo distance calculation.

When I applied this patch to the trunk, it took 2 hr 20mins to execute. I 
couldn't complete the run for the trunk code as there was trouble with my 
machine. But it wasn't complete after 4 hours. 

I will re-run and post the correct results. 


> Speed up distance calculations for sparse vectors
> -------------------------------------------------
>
>                 Key: MAHOUT-121
>                 URL: https://issues.apache.org/jira/browse/MAHOUT-121
>             Project: Mahout
>          Issue Type: Improvement
>          Components: Matrix
>            Reporter: Shashikant Kore
>         Attachments: mahout-121.patch, Mahout1211.patch
>
>
> From my mail to the Mahout mailing list.
> I am working on clustering a dataset which has thousands of sparse vectors. 
> The complete dataset has few tens of thousands of feature items but each 
> vector has only couple of hundred feature items. For this, there is an 
> optimization in distance calculation, a link to which I found the archives of 
> Mahout mailing list.
> http://lingpipe-blog.com/2009/03/12/speeding-up-k-means-clustering-algebra-sparse-vectors/
> I tried out this optimization.  The test setup had 2000 document  vectors 
> with few hundred items.  I ran canopy generation with Euclidean distance and 
> t1, t2 values as 250 and 200.
>  
> Current Canopy Generation: 28 min 15 sec.
> Canopy Generation with distance optimization: 1 min 38 sec.
> I know by experience that using Integer, Double objects instead of primitives 
> is computationally expensive. I changed the sparse vector  implementation to 
> used primitive collections by Trove [
> http://trove4j.sourceforge.net/ ].
> Distance optimization with Trove: 59 sec
> Current canopy generation with Trove: 21 min 55 sec
> To sum, these two optimizations reduced cluster generation time by a 97%.
> Currently, I have made the changes for Euclidean Distance, Canopy and KMeans. 
>  
> Licensing of Trove seems to be an issue which needs to be addressed.

-- 
This message is automatically generated by JIRA.
-
You can reply to this email to add a comment to the issue online.

Reply via email to