Если у вас есть векторы, вы можете запустить KMeansDriver. Вот помощь для того же.
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
Обновление: получить каталог результатов из HDFS в локальную фс. Затем используйте утилиту ClusterDumper, чтобы получить кластер и список документов в этом кластере.