mahout lucene document clustering howto?

Question

I'm reading that i can create mahout vectors from a lucene index that can be used to apply the mahout clustering algorithms. http://cwiki.apache.org/confluence/display/MAHOUT/Creating+Vectors+from+Text

I would like to apply K-means clustering algorithm in the documents in my Lucene index, but it is not clear how can i apply this algorithm (or hierarchical clustering) to extract meaningful clusters with these documents.

In this page http://cwiki.apache.org/confluence/display/MAHOUT/k-Means says that the algorithm accepts two input directories: one for the data points and one for the initial clusters. My data points are the documents? How can i "declare" that these are my documents (or their vectors) , simply take them and do the clustering?

sorry in advance for my poor grammar

Thank you

Answer 1

If you have vectors, you can run KMeansDriver. Here is the help for the same.

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

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.