List of cluster() Examples

Examples of cluster()

cc.mallet.cluster.Clusterer.cluster()
Return a clustering of an InstanceList
cc.mallet.cluster.GreedyAgglomerativeByDensity.cluster()
co.paralleluniverse.galaxy.Grid.cluster()
Returns the grid's cluster management and node lifecycle service. @return The grid's cluster management and node lifecycle service.
com.dotcms.repackage.org.elasticsearch.client.AdminClient.cluster()
edu.cmu.sphinx.speakerid.SpeakerIdentification.cluster()
@param inputFileName The name of the file used for diarization @return A cluster for each speaker found in the input file
edu.gslis.ttg.clusters.clusterers.SimpleJaccardClusterer.cluster()
edu.ucla.sspace.clustering.Clustering.cluster()
Clusters the set of rows in the given {@code Matrix} without a specifiednumber of clusters (optional operation). The set of cluster assignments are returned for each row in the matrix. @param matrix the {@link Matrix} whose row data points are to beclustered @param props the properties to use for any parameters each clusteringalgorithm may need @return an array of {@link Assignment} instances that indicate zero ormore clusters to which each row belongs.
edu.ucla.sspace.graph.LinkClustering.cluster()
Computes the similarity of the graph's edges and merges them until the specified number of clusters has been reached. @param numClusters the number of clusters to return
net.kuujo.vertigo.network.NetworkContext.cluster()
Returns the address of the cluster to which the network belongs. @return The address of the cluster to which the network belongs.
org.apache.kafka.common.requests.MetadataResponse.cluster()
org.apache.mahout.clustering.dirichlet.DirichletClusterer.cluster()
Iterate over the sample data, obtaining cluster samples periodically and returning them. @param numIterations the int number of iterations to perform @return a List>> of the observed models
org.apache.mahout.clustering.streaming.cluster.BallKMeans.cluster()
Clusters the datapoints in the list doing either random seeding of the centroids or k-means++. @param datapoints the points to be clustered. @return an UpdatableSearcher with the resulting clusters.
org.apache.mahout.clustering.streaming.cluster.StreamingKMeans.cluster()
Cluster the rows of a matrix, treating them as Centroids with weight 1. @param data matrix whose rows are to be clustered. @return the UpdatableSearcher containing the resulting centroids.

Examples of org.apache.mahout.clustering.dirichlet.DirichletClusterer.cluster()

                                        int numIterations,
                                        double alpha0,
                                        int thin,
                                        int burnin) {
    DirichletClusterer dc = new DirichletClusterer(SAMPLE_DATA, modelDist, alpha0, numClusters, thin, burnin);
    List<Cluster[]> result = dc.cluster(numIterations);
    printModels(result, burnin);
    for (Cluster[] models : result) {
      List<Cluster> clusters = new ArrayList<Cluster>();
      for (Cluster cluster : models) {
        if (isSignificant(cluster)) {

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Examples of org.apache.mahout.clustering.dirichlet.DirichletClusterer.cluster()

                                                      int numIterations,
                                                      double alpha0,
                                                      int thin,
                                                      int burnin) {
    DirichletClusterer dc = new DirichletClusterer(SAMPLE_DATA, modelDist, alpha0, numClusters, thin, burnin);
    List<Cluster[]> result = dc.cluster(numIterations);
    printModels(result, burnin);
    for (Cluster[] models : result) {
      List<Cluster> clusters = Lists.newArrayList();
      for (Cluster cluster : models) {
        if (isSignificant(cluster)) {

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Examples of org.apache.mahout.clustering.dirichlet.DirichletClusterer.cluster()

                                                      int numIterations,
                                                      double alpha0,
                                                      int thin,
                                                      int burnin) {
    DirichletClusterer dc = new DirichletClusterer(SAMPLE_DATA, modelDist, alpha0, numClusters, thin, burnin);
    List<Cluster[]> result = dc.cluster(numIterations);
    printModels(result, burnin);
    for (Cluster[] models : result) {
      List<Cluster> clusters = new ArrayList<Cluster>();
      for (Cluster cluster : models) {
        if (isSignificant(cluster)) {

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Examples of org.apache.mahout.clustering.streaming.cluster.BallKMeans.cluster()

    float testProbability = conf.getFloat(StreamingKMeansDriver.TEST_PROBABILITY, 0.1f);
    int numRuns = conf.getInt(StreamingKMeansDriver.NUM_BALLKMEANS_RUNS, 3);


    BallKMeans clusterer = new BallKMeans(StreamingKMeansUtilsMR.searcherFromConfiguration(conf),
        numClusters, maxNumIterations, trimFraction, kMeansPlusPlusInit, correctWeights, testProbability, numRuns);
    return clusterer.cluster(centroids);
  }
}

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Examples of org.apache.mahout.clustering.streaming.cluster.BallKMeans.cluster()

    float testProbability = conf.getFloat(StreamingKMeansDriver.TEST_PROBABILITY, 0.1f);
    int numRuns = conf.getInt(StreamingKMeansDriver.NUM_BALLKMEANS_RUNS, 3);


    BallKMeans ballKMeansCluster = new BallKMeans(StreamingKMeansUtilsMR.searcherFromConfiguration(conf),
        numClusters, maxNumIterations, trimFraction, kMeansPlusPlusInit, correctWeights, testProbability, numRuns);
    return ballKMeansCluster.cluster(centroids);
  }
}

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Examples of org.apache.mahout.clustering.streaming.cluster.StreamingKMeans.cluster()

      estimateDistanceCutoff = ClusteringUtils.estimateDistanceCutoff(estimatePoints, searcher.getDistanceMeasure());
    }


    StreamingKMeans clusterer = new StreamingKMeans(searcher, numClusters, estimateDistanceCutoff);
    while (datapointsIterator.hasNext()) {
      clusterer.cluster(datapointsIterator.next());
    }
    clusterer.reindexCentroids();


    return clusterer;
  }

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Examples of org.apache.mahout.clustering.streaming.cluster.StreamingKMeans.cluster()


    // Clusters the data using local batch StreamingKMeans.
    StreamingKMeans batchClusterer =
        new StreamingKMeans(StreamingKMeansUtilsMR.searcherFromConfiguration(configuration),
            mapDriver.getConfiguration().getInt("estimatedNumMapClusters", -1), DISTANCE_CUTOFF);
    batchClusterer.cluster(syntheticData.getFirst());
    List<Centroid> batchCentroids = Lists.newArrayList();
    for (Vector v : batchClusterer) {
      batchCentroids.add((Centroid) v);
    }

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Examples of org.apache.mahout.clustering.streaming.cluster.StreamingKMeans.cluster()

    // Clusters the data using point by point StreamingKMeans.
    StreamingKMeans perPointClusterer =
        new StreamingKMeans(StreamingKMeansUtilsMR.searcherFromConfiguration(configuration),
            (1 << NUM_DIMENSIONS) * (int)Math.log(NUM_DATA_POINTS), DISTANCE_CUTOFF);
    for (Centroid datapoint : syntheticData.getFirst()) {
      perPointClusterer.cluster(datapoint);
    }
    List<Centroid> perPointCentroids = Lists.newArrayList();
    for (Vector v : perPointClusterer) {
      perPointCentroids.add((Centroid) v);
    }

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Examples of org.apache.mahout.clustering.streaming.cluster.StreamingKMeans.cluster()

    StreamingKMeans clusterer =
        new StreamingKMeans(StreamingKMeansUtilsMR .searcherFromConfiguration(configuration),
            (1 << NUM_DIMENSIONS) * (int)Math.log(NUM_DATA_POINTS), DISTANCE_CUTOFF);


    long start = System.currentTimeMillis();
    clusterer.cluster(syntheticData.getFirst());
    long end = System.currentTimeMillis();


    System.out.printf("%f [s]\n", (end - start) / 1000.0);
    List<CentroidWritable> reducerInputs = Lists.newArrayList();
    int postMapperTotalWeight = 0;

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Examples of org.apache.mahout.clustering.streaming.cluster.StreamingKMeans.cluster()

    if (!dataPointsIterator.hasNext()) {
      dataPointsIterator = dataPoints.iterator();
    }


    while (dataPointsIterator.hasNext()) {
      streamingKMeans.cluster(dataPointsIterator.next());
    }


    streamingKMeans.reindexCentroids();
    return streamingKMeans;
  }

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