| Package | Description | 
|---|---|
| org.hipparchus.clustering | Clustering algorithms. | 
| org.hipparchus.clustering.distance | Common distance measures. | 
| org.hipparchus.clustering.evaluation | Cluster evaluation methods. | 
| Modifier and Type | Method and Description | 
|---|---|
| DistanceMeasure | Clusterer. getDistanceMeasure()Returns the  DistanceMeasureinstance used by this clusterer. | 
| Constructor and Description | 
|---|
| Clusterer(DistanceMeasure measure)Build a new clusterer with the given  DistanceMeasure. | 
| DBSCANClusterer(double eps,
               int minPts,
               DistanceMeasure measure)Creates a new instance of a DBSCANClusterer. | 
| FuzzyKMeansClusterer(int k,
                    double fuzziness,
                    int maxIterations,
                    DistanceMeasure measure)Creates a new instance of a FuzzyKMeansClusterer. | 
| FuzzyKMeansClusterer(int k,
                    double fuzziness,
                    int maxIterations,
                    DistanceMeasure measure,
                    double epsilon,
                    RandomGenerator random)Creates a new instance of a FuzzyKMeansClusterer. | 
| KMeansPlusPlusClusterer(int k,
                       int maxIterations,
                       DistanceMeasure measure)Build a clusterer. | 
| KMeansPlusPlusClusterer(int k,
                       int maxIterations,
                       DistanceMeasure measure,
                       RandomGenerator random)Build a clusterer. | 
| KMeansPlusPlusClusterer(int k,
                       int maxIterations,
                       DistanceMeasure measure,
                       RandomGenerator random,
                       KMeansPlusPlusClusterer.EmptyClusterStrategy emptyStrategy)Build a clusterer. | 
| Modifier and Type | Class and Description | 
|---|---|
| class  | CanberraDistanceCalculates the Canberra distance between two points. | 
| class  | ChebyshevDistanceCalculates the L∞ (max of abs) distance between two points. | 
| class  | EarthMoversDistanceCalculates the Earh Mover's distance (also known as Wasserstein metric) between two distributions. | 
| class  | EuclideanDistanceCalculates the L2 (Euclidean) distance between two points. | 
| class  | ManhattanDistanceCalculates the L1 (sum of abs) distance between two points. | 
| Constructor and Description | 
|---|
| ClusterEvaluator(DistanceMeasure measure)Creates a new cluster evaluator with the given distance measure. | 
| SumOfClusterVariances(DistanceMeasure measure) | 
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