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https://issues.apache.org/jira/browse/MAHOUT-121?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=12719546#action_12719546
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Shashikant Kore commented on MAHOUT-121:
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Sean,
Your patch has definitely improved the performance. For my test data set, it
took 8m 34s to generate canopies. The trunk code has been running for 40
minutes and still only 85% of Mapper is complete. I increased the default size
to 1024 and it took 5m 21s.
But, this is higher than Trove's 3m 34s. The array copy operation on addition
of new element to vector seems to be the culprit. Think centroid calculation
with has thousands of feature items. This could be solved only with a hash
implementation like Trove, which has (amortized) insertion and lookup time of
O(1).
> 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.
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