All Classes

Class	Description
CanberraDistance	Calculates the Canberra distance between two points.
CentroidCluster<T extends Clusterable>	A Cluster used by centroid-based clustering algorithms.
ChebyshevDistance	Calculates the L∞ (max of abs) distance between two points.
Cluster<T extends Clusterable>	Cluster holding a set of Clusterable points.
Clusterable	Interface for n-dimensional points that can be clustered together.
Clusterer<T extends Clusterable>	Base class for clustering algorithms.
ClusterEvaluator<T extends Clusterable>	Base class for cluster evaluation methods.
DBSCANClusterer<T extends Clusterable>	DBSCAN (density-based spatial clustering of applications with noise) algorithm.
DistanceMeasure	Interface for distance measures of n-dimensional vectors.
DoublePoint	A simple implementation of Clusterable for points with double coordinates.
EarthMoversDistance	Calculates the Earh Mover's distance (also known as Wasserstein metric) between two distributions.
EuclideanDistance	Calculates the L2 (Euclidean) distance between two points.
FuzzyKMeansClusterer<T extends Clusterable>	Fuzzy K-Means clustering algorithm.
KMeansPlusPlusClusterer<T extends Clusterable>	Clustering algorithm based on David Arthur and Sergei Vassilvitski k-means++ algorithm.
KMeansPlusPlusClusterer.EmptyClusterStrategy	Strategies to use for replacing an empty cluster.
LocalizedClusteringFormats	Enumeration for localized messages formats used in exceptions messages.
ManhattanDistance	Calculates the L1 (sum of abs) distance between two points.
MultiKMeansPlusPlusClusterer<T extends Clusterable>	A wrapper around a k-means++ clustering algorithm which performs multiple trials and returns the best solution.
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 \( c_i \).