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

Packages that use DistanceMeasure
org.apache.mahout.clustering: This package provides several clustering algorithm implementations.
org.apache.mahout.clustering.canopy
org.apache.mahout.clustering.dirichlet.models
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.clustering.spectral.kmeans
org.apache.mahout.common.distance

Uses of DistanceMeasure in org.apache.mahout.clustering

Fields in org.apache.mahout.clustering declared as DistanceMeasure
`protected DistanceMeasure` `DistanceMeasureCluster.measure`

Methods in org.apache.mahout.clustering that return DistanceMeasure
`DistanceMeasure` `JsonDistanceMeasureAdapter.deserialize(com.google.gson.JsonElement json, java.lang.reflect.Type typeOfT, com.google.gson.JsonDeserializationContext context)`
`DistanceMeasure` `DistanceMeasureCluster.getMeasure()`

Methods in org.apache.mahout.clustering with parameters of type DistanceMeasure
`com.google.gson.JsonElement` `JsonDistanceMeasureAdapter.serialize(DistanceMeasure src, java.lang.reflect.Type typeOfSrc, com.google.gson.JsonSerializationContext context)`
`void` `DistanceMeasureCluster.setMeasure(DistanceMeasure measure)`

Constructors in org.apache.mahout.clustering with parameters of type DistanceMeasure
`DistanceMeasureCluster(Vector point, int id, DistanceMeasure measure)`

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

Methods in org.apache.mahout.clustering.canopy with parameters of type DistanceMeasure
`static org.apache.hadoop.fs.Path` `CanopyDriver.buildClusters(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, boolean runSequential)` Build a directory of Canopy clusters from the input vectors and other arguments.
`static void` `CanopyDriver.clusterData(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path points, org.apache.hadoop.fs.Path canopies, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, boolean runSequential)`
`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.
`static void` `CanopyDriver.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, boolean runClustering, boolean runSequential)` Build a directory of Canopy clusters from the input arguments and, if requested, cluster the input vectors using these clusters
`static void` `CanopyDriver.run(org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, boolean runClustering, boolean runSequential)` Convenience method creates new Configuration() Build a directory of Canopy clusters from the input arguments and, if requested, cluster the input vectors using these clusters

Constructors in org.apache.mahout.clustering.canopy with parameters of type DistanceMeasure
`Canopy(Vector center, int canopyId, DistanceMeasure measure)` Create a new Canopy containing the given point and canopyId
`CanopyClusterer(DistanceMeasure measure, double t1, double t2)`

Uses of DistanceMeasure in org.apache.mahout.clustering.dirichlet.models

Methods in org.apache.mahout.clustering.dirichlet.models that return DistanceMeasure
`DistanceMeasure` `DistanceMeasureClusterDistribution.getMeasure()`

Methods in org.apache.mahout.clustering.dirichlet.models with parameters of type DistanceMeasure
`void` `DistanceMeasureClusterDistribution.setMeasure(DistanceMeasure measure)`

Constructors in org.apache.mahout.clustering.dirichlet.models with parameters of type DistanceMeasure
`DistanceMeasureClusterDistribution(VectorWritable modelPrototype, DistanceMeasure measure)`

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 org.apache.hadoop.fs.Path` `FuzzyKMeansDriver.buildClusters(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, int maxIterations, float m, boolean runSequential)` Iterate over the input vectors to produce cluster directories for each iteration
`static void` `FuzzyKMeansDriver.clusterData(org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, float m, boolean emitMostLikely, double threshold, boolean runSequential)` Run the job using supplied arguments
`static java.util.List<java.util.List<SoftCluster>>` `FuzzyKMeansClusterer.clusterPoints(java.lang.Iterable<Vector> points, java.util.List<SoftCluster> clusters, DistanceMeasure measure, double threshold, double m, int numIter)` This is the reference k-means implementation.

`static void`

`FuzzyKMeansDriver.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, int maxIterations, float m, boolean runClustering, boolean emitMostLikely, double threshold, boolean runSequential)`
Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to cluster the input vectors.

`static void`

`FuzzyKMeansDriver.run(org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, int maxIterations, float m, boolean runClustering, boolean emitMostLikely, double threshold, boolean runSequential)`
Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to cluster the input vectors.

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.
`SoftCluster(Vector center, int clusterId, DistanceMeasure measure)` Construct a new SoftCluster with the given point as its center

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

Methods in org.apache.mahout.clustering.kmeans with parameters of type DistanceMeasure
`static org.apache.hadoop.fs.Path` `KMeansDriver.buildClusters(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, int maxIterations, java.lang.String delta, boolean runSequential)` Iterate over the input vectors to produce cluster directories for each iteration
`static org.apache.hadoop.fs.Path` `RandomSeedGenerator.buildRandom(org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, int k, DistanceMeasure measure)`
`static void` `KMeansDriver.clusterData(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, java.lang.String convergenceDelta, boolean runSequential)` Run the job using supplied arguments
`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.
`boolean` `Cluster.computeConvergence(DistanceMeasure measure, double convergenceDelta)` Return if the cluster is converged by comparing its center and centroid.

`static void`

`KMeansDriver.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, int maxIterations, boolean runClustering, boolean runSequential)`
Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to cluster the input vectors.

`static void` `KMeansDriver.run(org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double convergenceDelta, int maxIterations, boolean runClustering, boolean runSequential)` Iterate over the input vectors to produce clusters and, if requested, use the results of the final iteration to cluster the input vectors.
`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)`

Constructors in org.apache.mahout.clustering.kmeans with parameters of type DistanceMeasure
`Cluster(Vector center, int clusterId, DistanceMeasure measure)` Construct a new cluster with the given point as its center
`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
`org.apache.hadoop.fs.Path` `MeanShiftCanopyDriver.buildClusters(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path clustersIn, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, double convergenceDelta, int maxIterations, boolean runSequential)` Iterate over the input clusters to produce the next cluster directories for each iteration
`static java.util.List<MeanShiftCanopy>` `MeanShiftCanopyClusterer.clusterPoints(java.lang.Iterable<Vector> points, DistanceMeasure measure, double convergenceThreshold, double t1, double t2, int numIter)` This is the reference mean-shift implementation.
`static void` `MeanShiftCanopyDriver.createCanopyFromVectors(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, DistanceMeasure measure, boolean runSequential)` Convert input vectors to MeanShiftCanopies for further processing
`void` `MeanShiftCanopyDriver.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, DistanceMeasure measure, double t1, double t2, double convergenceDelta, int maxIterations, boolean inputIsCanopies, boolean runClustering, boolean runSequential)` Run the job where the input format can be either Vectors or Canopies.

Constructors in org.apache.mahout.clustering.meanshift with parameters of type DistanceMeasure
`MeanShiftCanopy(Vector point, int id, DistanceMeasure measure)` Create a new Canopy containing the given point
`MeanShiftCanopyClusterer(DistanceMeasure aMeasure, double aT1, double aT2, double aDelta)`

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

Methods in org.apache.mahout.clustering.spectral.kmeans with parameters of type DistanceMeasure
`static void` `SpectralKMeansDriver.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, int numDims, int clusters, DistanceMeasure measure, double convergenceDelta, int maxIterations)` Run the Spectral KMeans clustering on the supplied arguments

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` `MahalanobisDistanceMeasure`
`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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