## 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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