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copied!<p>If you have vectors, you can run KMeansDriver. Here is the help for the same.</p> <pre><code>Usage: [--input <input> --clusters <clusters> --output <output> --distance <distance> --convergence <convergence> --max <max> --numReduce <numReduce> --k <k> --vectorClass <vectorClass> --overwrite --help] Options --input (-i) input The Path for input Vectors. Must be a SequenceFile of Writable, Vector --clusters (-c) clusters The input centroids, as Vectors. Must be a SequenceFile of Writable, Cluster/Canopy. If k is also specified, then a random set of vectors will be selected and written out to this path first --output (-o) output The Path to put the output in --distance (-m) distance The Distance Measure to use. Default is SquaredEuclidean --convergence (-d) convergence The threshold below which the clusters are considered to be converged. Default is 0.5 --max (-x) max The maximum number of iterations to perform. Default is 20 --numReduce (-r) numReduce The number of reduce tasks --k (-k) k The k in k-Means. If specified, then a random selection of k Vectors will be chosen as the Centroid and written to the clusters output path. --vectorClass (-v) vectorClass The Vector implementation class name. Default is SparseVector.class --overwrite (-w) If set, overwrite the output directory --help (-h) Print out help </code></pre> <p>Update: Get the result directory from HDFS to local fs. Then use ClusterDumper utility to get the cluster and list of documents in that cluster.</p>

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