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

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

    [ https://issues.apache.org/jira/browse/MAHOUT-1351?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=13824988#comment-13824988 ] 

Hudson commented on MAHOUT-1351:
--------------------------------

SUCCESS: Integrated in Mahout-Quality #2324 (See [https://builds.apache.org/job/Mahout-Quality/2324/])
MAHOUT-1351:Adding DenseVector support to AbstractCluster (smarthi: rev 1542650)
* /mahout/trunk/CHANGELOG
* /mahout/trunk/core/src/main/java/org/apache/mahout/clustering/AbstractCluster.java
* /mahout/trunk/core/src/test/java/org/apache/mahout/clustering/kmeans/TestKmeansClustering.java


> 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
>            Assignee: Suneel Marthi
>            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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