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  1. Mahout
  2. MAHOUT-1351

Adding DenseVector support to AbstractCluster

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Details

    • Improvement
    • Status: Closed
    • Minor
    • Resolution: Fixed
    • 0.8
    • 0.9
    • Clustering

    Description

      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.

      Attachments

        1. MAHOUT-1351.patch
          5 kB
          Dave DeBarr

        Activity

          People

            smarthi Suneel Marthi
            debarr Dave DeBarr
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