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From Ted Dunning <ted.dunn...@gmail.com>
Subject Re: [jira] [Updated] (MAHOUT-1351) Adding DenseVector support to AbstractCluster
Date Wed, 06 Nov 2013 02:04:28 GMT
Dave,

This is great.

Do you know if the new streaming k-means has the same problem?




On Tue, Nov 5, 2013 at 3:02 PM, Dave DeBarr (JIRA) <jira@apache.org> wrote:

>
>      [
> https://issues.apache.org/jira/browse/MAHOUT-1351?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel]
>
> Dave DeBarr updated MAHOUT-1351:
> --------------------------------
>
>     Status: Patch Available  (was: Open)
>
> This simple "svn diff" (patch) resolves issue MAHOUT-1351
>
> > Adding DenseVector support to AbstractCluster
> > ---------------------------------------------
> >
> >                 Key: MAHOUT-1351
> >                 URL: https://issues.apache.org/jira/browse/MAHOUT-1351
> >             Project: Mahout
> >          Issue Type: Improvement
> >          Components: Clustering
> >    Affects Versions: 0.8
> >            Reporter: Dave DeBarr
> >            Priority: Minor
> >              Labels: performance
> >             Fix For: 0.9
> >
> >         Attachments: MAHOUT-1351.patch
> >
> >   Original Estimate: 1h
> >  Remaining Estimate: 1h
> >
> > This improvement reduces runtime by 80% when performing k-means
> clustering of Scale Invariant Feature Transform (SIFT) descriptors to
> derive visual words for computer vision.  Unlike sparse document vectors,
> SIFT descriptors are dense.  This improvement involves updating the
> org.apache.mahout.clustering.AbstractCluster(Vector point, int id2)
> constructor to use "point.clone()" instead of "new
> RandomAccessSparseVector(point)" for creating the centroid.  Also added
> testKMeansSeqJobDenseVector() test for DenseVector processing.
>
>
>
> --
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