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Packages that use DistanceMeasure | |
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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 |
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Methods in org.apache.mahout.clustering.canopy with parameters of type DistanceMeasure | |
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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 | |
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CanopyClusterer(DistanceMeasure measure,
double t1,
double t2)
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Uses of DistanceMeasure in org.apache.mahout.clustering.fuzzykmeans |
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Methods in org.apache.mahout.clustering.fuzzykmeans that return DistanceMeasure | |
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DistanceMeasure |
FuzzyKMeansClusterer.getMeasure()
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Methods in org.apache.mahout.clustering.fuzzykmeans with parameters of type DistanceMeasure | |
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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 | |
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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 |
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Methods in org.apache.mahout.clustering.kmeans with parameters of type DistanceMeasure | |
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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 | |
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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 |
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Methods in org.apache.mahout.clustering.meanshift with parameters of type DistanceMeasure | |
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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 | |
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MeanShiftCanopyClusterer(DistanceMeasure aMeasure,
double aT1,
double aT2,
double aDelta)
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Uses of DistanceMeasure in org.apache.mahout.common.distance |
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Classes in org.apache.mahout.common.distance that implement DistanceMeasure | |
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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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