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

Packages that use DistanceMeasure
org.apache.mahout.clustering
org.apache.mahout.clustering.canopy
org.apache.mahout.clustering.fuzzykmeans
org.apache.mahout.clustering.iterator
org.apache.mahout.clustering.kmeans: This package provides an implementation of the k-means clustering algorithm.
org.apache.mahout.clustering.spectral.kmeans
org.apache.mahout.clustering.streaming.cluster
org.apache.mahout.common.distance
org.apache.mahout.math.hadoop.similarity
org.apache.mahout.math.neighborhood

Uses of DistanceMeasure in org.apache.mahout.clustering

Methods in org.apache.mahout.clustering with parameters of type DistanceMeasure
`static double` `ClusteringUtils.daviesBouldinIndex(List<? extends Vector> centroids, DistanceMeasure distanceMeasure, List<OnlineSummarizer> clusterDistanceSummaries)` Computes the Davies-Bouldin Index for a given clustering.
`static double` `ClusteringUtils.dunnIndex(List<? extends Vector> centroids, DistanceMeasure distanceMeasure, List<OnlineSummarizer> clusterDistanceSummaries)` Computes the Dunn Index of a given clustering.
`static double` `ClusteringUtils.estimateDistanceCutoff(Iterable<? extends Vector> data, DistanceMeasure distanceMeasure, int sampleLimit)`
`static double` `ClusteringUtils.estimateDistanceCutoff(List<? extends Vector> data, DistanceMeasure distanceMeasure)` Estimates the distance cutoff.
`static Matrix` `ClusteringUtils.getConfusionMatrix(List<? extends Vector> rowCentroids, List<? extends Vector> columnCentroids, Iterable<? extends Vector> datapoints, DistanceMeasure distanceMeasure)` Creates a confusion matrix by searching for the closest cluster of both the row clustering and column clustering of a point and adding its weight to that cell of the matrix.
`static List<OnlineSummarizer>` `ClusteringUtils.summarizeClusterDistances(Iterable<? extends Vector> datapoints, Iterable<? extends Vector> centroids, DistanceMeasure distanceMeasure)` Computes the summaries for the distances in each cluster.

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, double t3, double t4, int clusterFilter, boolean runSequential)` Build a directory of Canopy clusters from the input vectors and other arguments.
`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, int clusterFilter, boolean runSequential)` Convenience method for backwards compatibility
`void` `CanopyClusterer.config(DistanceMeasure aMeasure, double aT1, double aT2)` Configure the Canopy for unit tests
`static List<Canopy>` `CanopyClusterer.createCanopies(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, double clusterClassificationThreshold, boolean runSequential)` Convenience method to provide backward compatibility

`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, double t3, double t4, int clusterFilter, boolean runClustering, double clusterClassificationThreshold, 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, double clusterClassificationThreshold, 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.fuzzykmeans

Constructors in org.apache.mahout.clustering.fuzzykmeans with parameters of type DistanceMeasure
`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.iterator

Methods in org.apache.mahout.clustering.iterator that return DistanceMeasure
`DistanceMeasure` `DistanceMeasureCluster.getMeasure()`

Methods in org.apache.mahout.clustering.iterator with parameters of type DistanceMeasure
`void` `DistanceMeasureCluster.setMeasure(DistanceMeasure measure)`

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

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` `EigenSeedGenerator.buildFromEigens(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, int k, DistanceMeasure measure)`
`static org.apache.hadoop.fs.Path` `RandomSeedGenerator.buildRandom(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path output, int k, DistanceMeasure measure)`
`boolean` `Kluster.computeConvergence(DistanceMeasure measure, double convergenceDelta)` Return if the cluster is converged by comparing its center and centroid.

Constructors in org.apache.mahout.clustering.kmeans with parameters of type DistanceMeasure
`Kluster(Vector center, int clusterId, DistanceMeasure measure)` Construct a new cluster with the given point as its center

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, org.apache.hadoop.fs.Path tempDir, boolean ssvd)`

`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, org.apache.hadoop.fs.Path tempDir, boolean ssvd, int numReducers, int blockHeight, int oversampling, int poweriters)`
Run the Spectral KMeans clustering on the supplied arguments

Uses of DistanceMeasure in org.apache.mahout.clustering.streaming.cluster

Methods in org.apache.mahout.clustering.streaming.cluster that return DistanceMeasure
`DistanceMeasure` `StreamingKMeans.getDistanceMeasure()`

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

Classes in org.apache.mahout.common.distance that implement DistanceMeasure
`class` `ChebyshevDistanceMeasure` This class implements a "Chebyshev distance" metric by finding the maximum difference between each coordinate.
`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` `MinkowskiDistanceMeasure` Implement Minkowski distance, a real-valued generalization of the integral L(n) distances: Manhattan = L1, Euclidean = L2.
`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.

Uses of DistanceMeasure in org.apache.mahout.math.hadoop.similarity

Methods in org.apache.mahout.math.hadoop.similarity with parameters of type DistanceMeasure
`static void` `VectorDistanceSimilarityJob.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path seeds, org.apache.hadoop.fs.Path output, DistanceMeasure measure, String outType)`
`static void` `VectorDistanceSimilarityJob.run(org.apache.hadoop.conf.Configuration conf, org.apache.hadoop.fs.Path input, org.apache.hadoop.fs.Path seeds, org.apache.hadoop.fs.Path output, DistanceMeasure measure, String outType, Double maxDistance)`

Uses of DistanceMeasure in org.apache.mahout.math.neighborhood

Fields in org.apache.mahout.math.neighborhood declared as DistanceMeasure
`protected DistanceMeasure` `Searcher.distanceMeasure`

Methods in org.apache.mahout.math.neighborhood that return DistanceMeasure
`DistanceMeasure` `Searcher.getDistanceMeasure()`

Constructors in org.apache.mahout.math.neighborhood with parameters of type DistanceMeasure
`BruteSearch(DistanceMeasure distanceMeasure)`
`FastProjectionSearch(DistanceMeasure distanceMeasure, int numProjections, int searchSize)`
`LocalitySensitiveHashSearch(DistanceMeasure distanceMeasure, int searchSize)`
`ProjectionSearch(DistanceMeasure distanceMeasure, int numProjections, int searchSize)`
`Searcher(DistanceMeasure distanceMeasure)`
`UpdatableSearcher(DistanceMeasure distanceMeasure)`

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