Examples of BallKMeans

org.apache.mahout.clustering.streaming.cluster.BallKMeans
Implements a ball k-means algorithm for weighted vectors with probabilistic seeding similar to k-means++. The idea is that k-means++ gives good starting clusters and ball k-means can tune up the final result very nicely in only a few passes (or even in a single iteration for well-clusterable data). A good reference for this class of algorithms is "The Effectiveness of Lloyd-Type Methods for the k-Means Problem" by Rafail Ostrovsky, Yuval Rabani, Leonard J. Schulman and Chaitanya Swamy. The code here uses the seeding strategy as described in section 4.1.1 of that paper and the ball k-means step as described in section 4.2. We support multiple iterations in contrast to the algorithm described in the paper.

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

    boolean kMeansPlusPlusInit = !conf.getBoolean(StreamingKMeansDriver.RANDOM_INIT, false);
    boolean correctWeights = !conf.getBoolean(StreamingKMeansDriver.IGNORE_WEIGHTS, false);
    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

    boolean kMeansPlusPlusInit = !conf.getBoolean(StreamingKMeansDriver.RANDOM_INIT, false);
    boolean correctWeights = !conf.getBoolean(StreamingKMeansDriver.IGNORE_WEIGHTS, false);
    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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