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Packages that use Cluster | |
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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 |
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Subclasses of Cluster in org.apache.mahout.clustering.fuzzykmeans | |
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class |
SoftCluster
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Methods in org.apache.mahout.clustering.fuzzykmeans with parameters of type Cluster | |
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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 |
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Methods in org.apache.mahout.clustering.kmeans that return types with arguments of type Cluster | |
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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 | |
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boolean |
KMeansClusterer.computeConvergence(Cluster cluster)
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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 | |
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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)
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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)
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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 |
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Subclasses of Cluster in org.apache.mahout.clustering.meanshift | |
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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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