Package org.apache.mahout.utils.clustering

Examples of org.apache.mahout.utils.clustering.ClusterDumper.printClusters()

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  KMeansDriver.run(svdData, new Path(output, "clusters-0"), kmeansOutput, measure,
        0.001, 10, true, 0.0, true);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(finalClusterPath(conf,
        kmeansOutput, 10), new Path(kmeansOutput, "clusteredPoints"));
    clusterDumper.printClusters(termDictionary);
  }
 
  // @Test
  public void testKmeansDSVD() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
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  KMeansDriver.run(sData.getRowPath(), new Path(output, "clusters-0"),
        kmeansOutput, measure, 0.001, 10, true, 0.0, true);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(finalClusterPath(conf,
        kmeansOutput, 10), new Path(kmeansOutput, "clusteredPoints"));
    clusterDumper.printClusters(termDictionary);
  }
 
  // @Test
  public void testKmeansDSVD2() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
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  KMeansDriver.run(sData.getRowPath(), new Path(output, "clusters-0"),
        kmeansOutput, measure, 0.001, 10, true, 0.0, true);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(finalClusterPath(conf,
        kmeansOutput, 10), new Path(kmeansOutput, "clusteredPoints"));
    clusterDumper.printClusters(termDictionary);
  }
}
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    FuzzyKMeansDriver.run(directoryContainingConvertedInput, new Path(canopyOutput, "clusters-0-final"), output, measure,
        convergenceDelta, maxIterations, fuzziness, true, true, 0.0, false);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(new Path(output, "clusters-*-final"), new Path(output,
        "clusteredPoints"));
    clusterDumper.printClusters(null);
  }
}
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    DirichletDriver.run(new Configuration(), directoryContainingConvertedInput, output, description, numModels,
        maxIterations, alpha0, true, emitMostLikely, threshold, false);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(new Path(output, "clusters-*-final"), new Path(output,
        "clusteredPoints"));
    clusterDumper.printClusters(null);
  }
 
  /**
   * Actually prints out the clusters
   *
 
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        measure, kernelProfile, t1, t2, convergenceDelta, maxIterations, true,
        true, false);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(new Path(output,
        "clusters-*-final"), new Path(output, "clusteredPoints"));
    clusterDumper.printClusters(null);
  }
 
}
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    CanopyDriver.run(new Configuration(), directoryContainingConvertedInput,
        output, measure, t1, t2, true, 0.0, false);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(new Path(output,
        "clusters-0-final"), new Path(output, "clusteredPoints"));
    clusterDumper.printClusters(null);
  }

  @Override
  public int run(String[] args) throws Exception {
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    KMeansDriver.run(conf, directoryContainingConvertedInput, clusters, output, measure, convergenceDelta,
        maxIterations, true, 0.0, false);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(new Path(output, "clusters-*-final"), new Path(output,
        "clusteredPoints"));
    clusterDumper.printClusters(null);
  }
 
  /**
   * Run the kmeans clustering job on an input dataset using the given distance measure, t1, t2 and iteration
   * parameters. All output data will be written to the output directory, which will be initially deleted if it exists.
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    KMeansDriver.run(conf, directoryContainingConvertedInput, new Path(canopyOutput, Cluster.INITIAL_CLUSTERS_DIR
        + "-final"), output, measure, convergenceDelta, maxIterations, true, 0.0, false);
    // run ClusterDumper
    ClusterDumper clusterDumper = new ClusterDumper(new Path(output, "clusters-*-final"), new Path(output,
        "clusteredPoints"));
    clusterDumper.printClusters(null);
  }
}
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    Path kMeansOutput = new Path(output, "kmeans");
    KMeansDriver.run(conf, getTestTempDirPath("testdata"), initialPoints, kMeansOutput, 0.001, 10, true, 0.0, false);
    // Print out clusters
    ClusterDumper clusterDumper = new ClusterDumper(finalClusterPath(conf,
            output, 10), new Path(kMeansOutput, "clusteredPoints"));
    clusterDumper.printClusters(termDictionary);
  }

  @Test
  public void testJsonClusterDumper() throws Exception {
    DistanceMeasure measure = new EuclideanDistanceMeasure();
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