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