Shashi,
I'm glad to see you have demonstrated the improvement made possible by
that optimization. It is really astounding. I will look over your
patches immediately.
Jeff
Shashikant Kore wrote:
Hi,
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. How do we go about pushing these changes to Mahout?
--shashi
