| 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  
DistanceMeasure instance 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  | 
CanberraDistance
Calculates the Canberra distance between two points. 
 | 
class  | 
ChebyshevDistance
Calculates the L∞ (max of abs) distance between two points. 
 | 
class  | 
EarthMoversDistance
Calculates the Earh Mover's distance (also known as Wasserstein metric) between two distributions. 
 | 
class  | 
EuclideanDistance
Calculates the L2 (Euclidean) distance between two points. 
 | 
class  | 
ManhattanDistance
Calculates 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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