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From Nicolás Fantone (JIRA) <j...@apache.org>
Subject [jira] Commented: (MAHOUT-121) Speed up distance calculations for sparse vectors
Date Wed, 29 Jul 2009 13:20:14 GMT

    [ https://issues.apache.org/jira/browse/MAHOUT-121?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12736627#action_12736627
] 

Nicolás Fantone commented on MAHOUT-121:
----------------------------------------

Hi all - I've just sign up to Jira!

Before committing this patch, could any of you take a look to my latest mail in the Mahout
mailing list (it's a bit extensive to copy/paste in here)? With some enlightenment, I could
create a patch from my work and we may be able to make bigger improvements for KMeans.

> 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
>            Assignee: Grant Ingersoll
>         Attachments: Canopy_Wiki_1000-2009-06-24.snapshot, doc-vector-4k, MAHOUT-121-cluster-distance.patch,
mahout-121.patch, MAHOUT-121.patch, MAHOUT-121.patch, MAHOUT-121.patch, MAHOUT-121.patch,
MAHOUT-121.patch, mahout-121.patch, MAHOUT-121jfe.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.

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