[MAHOUT-1351] Adding DenseVector support to AbstractCluster

• Type: Improvement
• Status: Closed
• Priority: Minor
• Resolution: Fixed
• Affects Version/s: 0.8
• Fix Version/s: 0.9
• Component/s: classic
• Labels: performance
• Assignee: Suneel Marthi
• Reporter: Dave DeBarr
• Created: 05/Nov/13 22:54
• Updated: 31/Jan/24 22:14
• Resolved: 17/Nov/13 05:45

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.