| Package | Description | 
|---|---|
| org.hipparchus.clustering | 
 Clustering algorithms. 
 | 
| org.hipparchus.clustering.evaluation | 
 Cluster evaluation methods. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
CentroidCluster<T extends Clusterable>
A Cluster used by centroid-based clustering algorithms. 
 | 
class  | 
Cluster<T extends Clusterable>
Cluster holding a set of  
Clusterable points. | 
class  | 
Clusterer<T extends Clusterable>
Base class for clustering algorithms. 
 | 
class  | 
DBSCANClusterer<T extends Clusterable>
DBSCAN (density-based spatial clustering of applications with noise) algorithm. 
 | 
class  | 
FuzzyKMeansClusterer<T extends Clusterable>
Fuzzy K-Means clustering algorithm. 
 | 
class  | 
KMeansPlusPlusClusterer<T extends Clusterable>
Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm. 
 | 
class  | 
MultiKMeansPlusPlusClusterer<T extends Clusterable>
A wrapper around a k-means++ clustering algorithm which performs multiple trials
 and returns the best solution. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
DoublePoint
A simple implementation of  
Clusterable for points with double coordinates. | 
| Modifier and Type | Method and Description | 
|---|---|
Clusterable | 
CentroidCluster.getCenter()
Get the point chosen to be the center of this cluster. 
 | 
| Modifier and Type | Method and Description | 
|---|---|
protected double | 
Clusterer.distance(Clusterable p1,
        Clusterable p2)
Calculates the distance between two  
Clusterable instances
 with the configured DistanceMeasure. | 
| Constructor and Description | 
|---|
CentroidCluster(Clusterable center)
Build a cluster centered at a specified point. 
 | 
| Modifier and Type | Class and Description | 
|---|---|
class  | 
ClusterEvaluator<T extends Clusterable>
Base class for cluster evaluation methods. 
 | 
class  | 
SumOfClusterVariances<T extends Clusterable>
Computes the sum of intra-cluster distance variances according to the formula:
 \] score = \sum\limits_{i=1}^n \sigma_i^2 \]
 where n is the number of clusters and \( \sigma_i^2 \) is the variance of
 intra-cluster distances of cluster \( c_i \). 
 | 
| Modifier and Type | Method and Description | 
|---|---|
protected Clusterable | 
ClusterEvaluator.centroidOf(Cluster<T> cluster)
Computes the centroid for a cluster. 
 | 
| Modifier and Type | Method and Description | 
|---|---|
protected double | 
ClusterEvaluator.distance(Clusterable p1,
        Clusterable p2)
Calculates the distance between two  
Clusterable instances
 with the configured DistanceMeasure. | 
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