Uses of Class
org.apache.mahout.clustering.kmeans.Cluster

Packages that use Cluster
org.apache.mahout.clustering.fuzzykmeans   
org.apache.mahout.clustering.kmeans This package provides an implementation of the k-means clustering algorithm. 
org.apache.mahout.clustering.meanshift   
 

Uses of Cluster in org.apache.mahout.clustering.fuzzykmeans
 

Subclasses of Cluster in org.apache.mahout.clustering.fuzzykmeans
 class SoftCluster
           
 

Methods in org.apache.mahout.clustering.fuzzykmeans with parameters of type Cluster
 boolean FuzzyKMeansClusterer.computeConvergence(Cluster cluster)
          Return if the cluster is converged by comparing its center and centroid.
 

Uses of Cluster in org.apache.mahout.clustering.kmeans
 

Methods in org.apache.mahout.clustering.kmeans that return types with arguments of type Cluster
static java.util.List<java.util.List<Cluster>> KMeansClusterer.clusterPoints(java.lang.Iterable<Vector> points, java.util.List<Cluster> clusters, DistanceMeasure measure, int maxIter, double distanceThreshold)
          This is the reference k-means implementation.
 

Methods in org.apache.mahout.clustering.kmeans with parameters of type Cluster
 boolean KMeansClusterer.computeConvergence(Cluster cluster)
           
static java.lang.String Cluster.formatCluster(Cluster cluster)
          Format the cluster for output
 

Method parameters in org.apache.mahout.clustering.kmeans with type arguments of type Cluster
protected  void KMeansClusterer.addPointToNearestCluster(Vector point, java.lang.Iterable<Cluster> clusters)
          Sequential implementation to add point to the nearest cluster
static java.util.List<java.util.List<Cluster>> KMeansClusterer.clusterPoints(java.lang.Iterable<Vector> points, java.util.List<Cluster> clusters, DistanceMeasure measure, int maxIter, double distanceThreshold)
          This is the reference k-means implementation.
 void KMeansClusterer.emitPointToNearestCluster(Vector point, java.lang.Iterable<Cluster> clusters, org.apache.hadoop.mapreduce.Mapper.Context context)
          Iterates over all clusters and identifies the one closes to the given point.
protected  void KMeansClusterer.emitPointToNearestCluster(Vector point, java.lang.Iterable<Cluster> clusters, org.apache.hadoop.io.SequenceFile.Writer writer)
          Iterates over all clusters and identifies the one closes to the given point.
 void KMeansClusterer.outputPointWithClusterInfo(Vector vector, java.lang.Iterable<Cluster> clusters, org.apache.hadoop.mapreduce.Mapper.Context context)
           
protected static boolean KMeansClusterer.runKMeansIteration(java.lang.Iterable<Vector> points, java.lang.Iterable<Cluster> clusters, DistanceMeasure measure, double distanceThreshold)
          Perform a single iteration over the points and clusters, assigning points to clusters and returning if the iterations are completed.
 void KMeansReducer.setup(java.util.List<Cluster> clusters, DistanceMeasure measure)
           
protected  boolean KMeansClusterer.testConvergence(java.lang.Iterable<Cluster> clusters, double distanceThreshold)
          Sequential implementation to test convergence and update cluster centers
 

Uses of Cluster in org.apache.mahout.clustering.meanshift
 

Subclasses of Cluster in org.apache.mahout.clustering.meanshift
 class MeanShiftCanopy
          This class models a canopy as a center point, the number of points that are contained within it according to the application of some distance metric, and a point total which is the sum of all the points and is used to compute the centroid when needed.
 



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