[jira] [Updated] (MAHOUT-1351) Adding DenseVector support to AbstractCluster

["Dave DeBarr (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MAHOUT-1351?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Dave DeBarr updated MAHOUT-1351:
--------------------------------

    Attachment: MAHOUT-1351.patch

An "svn diff" to resolve 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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