Uses of Interface
org.apache.mahout.common.distance.DistanceMeasure

Packages that use DistanceMeasure
org.apache.mahout.clustering.canopy   
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   
org.apache.mahout.common.distance   
 

Uses of DistanceMeasure in org.apache.mahout.clustering.canopy
 

Methods in org.apache.mahout.clustering.canopy with parameters of type DistanceMeasure
 void CanopyClusterer.config(DistanceMeasure aMeasure, double aT1, double aT2)
          Configure the Canopy for unit tests
static java.util.List<Canopy> CanopyClusterer.createCanopies(java.util.List<Vector> points, DistanceMeasure measure, double t1, double t2)
          Iterate through the points, adding new canopies.
 

Constructors in org.apache.mahout.clustering.canopy with parameters of type DistanceMeasure
CanopyClusterer(DistanceMeasure measure, double t1, double t2)
           
 

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

Methods in org.apache.mahout.clustering.fuzzykmeans that return DistanceMeasure
 DistanceMeasure FuzzyKMeansClusterer.getMeasure()
           
 

Methods in org.apache.mahout.clustering.fuzzykmeans with parameters of type DistanceMeasure
static java.util.List<java.util.List<SoftCluster>> FuzzyKMeansClusterer.clusterPoints(java.util.List<Vector> points, java.util.List<SoftCluster> clusters, DistanceMeasure measure, double threshold, double m, int numIter)
          This is the reference k-means implementation.
 

Constructors in org.apache.mahout.clustering.fuzzykmeans with parameters of type DistanceMeasure
FuzzyKMeansClusterer(DistanceMeasure measure, double convergenceDelta, double m)
          Init the fuzzy k-means clusterer with the distance measure to use for comparison.
 

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

Methods in org.apache.mahout.clustering.kmeans with parameters of type DistanceMeasure
static java.util.List<java.util.List<Cluster>> KMeansClusterer.clusterPoints(java.util.List<Vector> points, java.util.List<Cluster> clusters, DistanceMeasure measure, int maxIter, double distanceThreshold)
          This is the reference k-means implementation.
 boolean Cluster.computeConvergence(DistanceMeasure measure, double convergenceDelta)
          Return if the cluster is converged by comparing its center and centroid.
static boolean KMeansClusterer.runKMeansIteration(java.util.List<Vector> points, java.util.List<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.
 

Constructors in org.apache.mahout.clustering.kmeans with parameters of type DistanceMeasure
KMeansClusterer(DistanceMeasure measure)
          Init the k-means clusterer with the distance measure to use for comparison.
 

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

Methods in org.apache.mahout.clustering.meanshift with parameters of type DistanceMeasure
static java.util.List<MeanShiftCanopy> MeanShiftCanopyClusterer.clusterPoints(java.util.List<Vector> points, DistanceMeasure measure, double convergenceThreshold, double t1, double t2, int numIter)
          This is the reference mean-shift implementation.
 void MeanShiftCanopyClusterer.config(DistanceMeasure aMeasure, double aT1, double aT2, double aDelta)
          Configure the Canopy for unit tests
 

Constructors in org.apache.mahout.clustering.meanshift with parameters of type DistanceMeasure
MeanShiftCanopyClusterer(DistanceMeasure aMeasure, double aT1, double aT2, double aDelta)
           
 

Uses of DistanceMeasure in org.apache.mahout.common.distance
 

Classes in org.apache.mahout.common.distance that implement DistanceMeasure
 class CosineDistanceMeasure
          This class implements a cosine distance metric by dividing the dot product of two vectors by the product of their lengths
 class EuclideanDistanceMeasure
          This class implements a Euclidean distance metric by summing the square root of the squared differences between each coordinate.
 class ManhattanDistanceMeasure
          This class implements a "manhattan distance" metric by summing the absolute values of the difference between each coordinate
 class SquaredEuclideanDistanceMeasure
          Like EuclideanDistanceMeasure but it does not take the square root.
 class TanimotoDistanceMeasure
          Tanimoto coefficient implementation.
 class WeightedDistanceMeasure
          Abstract implementation of DistanceMeasure with support for weights.
 class WeightedEuclideanDistanceMeasure
          This class implements a Euclidean distance metric by summing the square root of the squared differences between each coordinate, optionally adding weights.
 class WeightedManhattanDistanceMeasure
          This class implements a "Manhattan distance" metric by summing the absolute values of the difference between each coordinate, optionally with weights.
 



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