Speed up distance calculations for sparse vectors
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Key: MAHOUT-121
URL: https://issues.apache.org/jira/browse/MAHOUT-121
Project: Mahout
Issue Type: Improvement
Components: Matrix
Reporter: Shashikant Kore
>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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