All Classes
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All Classes Interface Summary Class Summary Enum Summary 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 ofClusterable
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 ofClusterable
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 \( c_i \).